Objectives

library(tidyverse)
library(knitr)
library(broom)
library(stringr)
library(modelr)
library(forcats)
library(ggmap)
library(plotly)

options(digits = 3)
set.seed(1234)
theme_set(theme_minimal())

Geospatial visualization

Geospatial visualizations are some of the oldest data visualization methods in human existence. Data maps were first popularized in the seventeenth century and have grown in complexity and detail since then. Consider Google Maps, the sheer volume of data depicted, and the analytical pathways available to its users.

Geometric visualizations are used to depict spatial features, and with the incorporation of data reveal additional attributes and information. The main features of a map are defined by its scale (the proportion between distances and sizes on the map), its projection (how the three-dimensional Earth is represented on a two-dimensional surface), and its symbols (how data is depicted and visualized on the map).1

Map boundaries

Drawing maps in R is a layer-like process. Typically you start by drawing the boundaries of the geographic regions you wish to visualize, then you add additional layers defined by other variables:

  • Points
  • Symbols
  • Lines
  • Fills (choropleths)

Let’s start by first drawing a map’s boundaries. Later on we will fill these in with data and turn them into data visualizations.

Storing map boundaries

A geographic information system (GIS) is software that is “designed to capture, store, manipulate, analyze, manage, and present spatial or geographic data”.2 There are many specialized software packages for spatial data analysis, many of which are commercial or proprietary software. For serious spatial data analysis tasks, you probably want to learn how to use these products. However for casual usage, R has a number of tools for drawing maps, most importantly ggplot2.

Using maps boundaries

The maps package includes the map() function for drawing maps based on bundled geodatabases using graphics package:

library(maps)

# map of the world
map()

# usa boundaries
map("usa")

map("state")

# county map of illinois
map("county", "illinois")

These are fine, but we’d rather use them with our friendly ggplot2 library. We can do this by converting the geodatabases into data frames for plotting with ggplot2.

# map of the world
map_data("world") %>%
  as_tibble
## # A tibble: 99,338 × 6
##     long   lat group order region subregion
##  * <dbl> <dbl> <dbl> <int>  <chr>     <chr>
##  1 -69.9  12.5     1     1  Aruba      <NA>
##  2 -69.9  12.4     1     2  Aruba      <NA>
##  3 -69.9  12.4     1     3  Aruba      <NA>
##  4 -70.0  12.5     1     4  Aruba      <NA>
##  5 -70.1  12.5     1     5  Aruba      <NA>
##  6 -70.1  12.6     1     6  Aruba      <NA>
##  7 -70.0  12.6     1     7  Aruba      <NA>
##  8 -70.0  12.6     1     8  Aruba      <NA>
##  9 -69.9  12.5     1     9  Aruba      <NA>
## 10 -69.9  12.5     1    10  Aruba      <NA>
## # ... with 99,328 more rows
# usa boundaries
map_data("usa") %>%
  as_tibble
## # A tibble: 7,243 × 6
##     long   lat group order region subregion
##  * <dbl> <dbl> <dbl> <int>  <chr>     <chr>
##  1  -101  29.7     1     1   main      <NA>
##  2  -101  29.7     1     2   main      <NA>
##  3  -101  29.7     1     3   main      <NA>
##  4  -101  29.6     1     4   main      <NA>
##  5  -101  29.6     1     5   main      <NA>
##  6  -101  29.6     1     6   main      <NA>
##  7  -101  29.6     1     7   main      <NA>
##  8  -101  29.6     1     8   main      <NA>
##  9  -101  29.6     1     9   main      <NA>
## 10  -101  29.6     1    10   main      <NA>
## # ... with 7,233 more rows
map_data("state") %>%
  as_tibble
## # A tibble: 15,537 × 6
##     long   lat group order  region subregion
##  * <dbl> <dbl> <dbl> <int>   <chr>     <chr>
##  1 -87.5  30.4     1     1 alabama      <NA>
##  2 -87.5  30.4     1     2 alabama      <NA>
##  3 -87.5  30.4     1     3 alabama      <NA>
##  4 -87.5  30.3     1     4 alabama      <NA>
##  5 -87.6  30.3     1     5 alabama      <NA>
##  6 -87.6  30.3     1     6 alabama      <NA>
##  7 -87.6  30.3     1     7 alabama      <NA>
##  8 -87.6  30.3     1     8 alabama      <NA>
##  9 -87.7  30.3     1     9 alabama      <NA>
## 10 -87.8  30.3     1    10 alabama      <NA>
## # ... with 15,527 more rows
# county map of illinois
map_data("county", "illinois") %>%
  as_tibble
## # A tibble: 1,697 × 6
##     long   lat group order   region subregion
##  * <dbl> <dbl> <dbl> <int>    <chr>     <chr>
##  1 -91.5  40.2     1     1 illinois     adams
##  2 -90.9  40.2     1     2 illinois     adams
##  3 -90.9  40.2     1     3 illinois     adams
##  4 -90.9  40.1     1     4 illinois     adams
##  5 -90.9  39.8     1     5 illinois     adams
##  6 -90.9  39.8     1     6 illinois     adams
##  7 -91.4  39.8     1     7 illinois     adams
##  8 -91.4  39.8     1     8 illinois     adams
##  9 -91.4  39.9     1     9 illinois     adams
## 10 -91.5  39.9     1    10 illinois     adams
## # ... with 1,687 more rows

map_data() converts the geodatabases into data frames where each row is a single point on the map. The resulting data frame contains the following variables:

  • long - longitude. Things to the west of the prime meridian are negative
  • lat - latitude
  • order - this identifies the order ggplot() should follow to “connect the dots” and draw the borders
  • region and subregion identify what region or subregion a set of points surrounds
  • group - this is perhaps the most important variable in the data frame. ggplot() uses the group aesthetic to determine whether adjacent points should be connected by a line. If they are in the same group, then the points are connected. If not, then they are not connected. This is the same basic principle for standard geom_line() plots:

    library(gapminder)
    
    # no group aesthetic
    ggplot(gapminder, aes(year, lifeExp)) +
      geom_line()

    # with grouping by country
    ggplot(gapminder, aes(year, lifeExp, group = country)) +
      geom_line()

Drawing the United States

Let’s draw a map of the United States. First we need to store the USA map boundaries in a data frame:

usa <- map_data("usa") %>%
  as_tibble
usa
## # A tibble: 7,243 × 6
##     long   lat group order region subregion
##  * <dbl> <dbl> <dbl> <int>  <chr>     <chr>
##  1  -101  29.7     1     1   main      <NA>
##  2  -101  29.7     1     2   main      <NA>
##  3  -101  29.7     1     3   main      <NA>
##  4  -101  29.6     1     4   main      <NA>
##  5  -101  29.6     1     5   main      <NA>
##  6  -101  29.6     1     6   main      <NA>
##  7  -101  29.6     1     7   main      <NA>
##  8  -101  29.6     1     8   main      <NA>
##  9  -101  29.6     1     9   main      <NA>
## 10  -101  29.6     1    10   main      <NA>
## # ... with 7,233 more rows

Simple black map

  • We can use geom_polygon() to draw lines between points and close them up (connect the last point with the first point)
  • x = long and y = lat
  • We map the group aesthetic to the group column
ggplot() +
  geom_polygon(data = usa, aes(x=long, y = lat, group = group))

Ta-da! A few things we want to immediately start thinking about. First, because latitude and longitude have absolute relations to one another, we need to fix the aspect ratio so that we don’t accidentially compress the graph in one dimension. Fixing the aspect ratio also means that even if you change the outer dimensions of the graph (i.e. adjust the window size), then the aspect ratio of the graph itself remains unchanged. We can do this using the coord_fixed() function:

ggplot() +
  geom_polygon(data = usa, aes(x=long, y = lat, group = group)) +
  coord_fixed()

Now it looks a little squished. You can play around with the aspect ratio to find a better projection:3

ggplot() +
  geom_polygon(data = usa, aes(x=long, y = lat, group = group)) +
  coord_fixed(1.3)

Change the colors

Since this is a ggplot() object, we can change the fill and color aesthetics for the map:

ggplot() +
  geom_polygon(data = usa, aes(x=long, y = lat, group = group),
               fill = NA, color = "red") + 
  coord_fixed(1.3)

gg1 <- ggplot() + 
  geom_polygon(data = usa, aes(x=long, y = lat, group = group),
               fill = "violet", color = "blue") + 
  coord_fixed(1.3)
gg1

Always remember to use the group aesthetic

What happens if we plot the map without the group aesthetic?

ggplot() + 
  geom_polygon(data = usa, aes(x=long, y = lat),
               fill = "violet", color = "blue") + 
  coord_fixed(1.3)

Oops. The map doesn’t connect the dots in the correct order.

State maps

maps also comes with a state boundaries geodatabase:

states <- map_data("state") %>%
  as_tibble()
states
## # A tibble: 15,537 × 6
##     long   lat group order  region subregion
##  * <dbl> <dbl> <dbl> <int>   <chr>     <chr>
##  1 -87.5  30.4     1     1 alabama      <NA>
##  2 -87.5  30.4     1     2 alabama      <NA>
##  3 -87.5  30.4     1     3 alabama      <NA>
##  4 -87.5  30.3     1     4 alabama      <NA>
##  5 -87.6  30.3     1     5 alabama      <NA>
##  6 -87.6  30.3     1     6 alabama      <NA>
##  7 -87.6  30.3     1     7 alabama      <NA>
##  8 -87.6  30.3     1     8 alabama      <NA>
##  9 -87.7  30.3     1     9 alabama      <NA>
## 10 -87.8  30.3     1    10 alabama      <NA>
## # ... with 15,527 more rows

By default, each state is filled with the same color:

ggplot(data = states) + 
  geom_polygon(aes(x = long, y = lat, group = group), color = "white") + 
  coord_fixed(1.3)

We can adjust this by using the fill aesthetic. Here, let’s map region to fill:

ggplot(data = states) + 
  geom_polygon(aes(x = long, y = lat, fill = region, group = group), color = "white") + 
  coord_fixed(1.3) +
  # turn off color legend
  theme(legend.position = "none")

Here, each state is assigned a different color at random. You can start to imagine how we might build a choropleth by mapping a different variable to fill, but we’ll return to that in a little bit.

Plot a subset of states

We can use filter() to subset the states data frame and draw a map with only a subset of the states. For example, if we want to only graph states in the Midwest:

midwest <- subset(states, region %in% c("illinois", "indiana", "iowa",
                                           "kansas", "michigan", "minnesota",
                                           "missouri", "nebraska", "north dakota",
                                           "ohio", "south dakota", "wisconsin"))

ggplot(data = midwest) + 
  geom_polygon(aes(x = long, y = lat, group = group),
               fill = "palegreen", color = "black") + 
  coord_fixed(1.3)

Zoom in on Illinois and its counties

First let’s get the Illinois boundaries:

(il_df <- filter(states, region == "illinois"))
## # A tibble: 329 × 6
##     long   lat group order   region subregion
##    <dbl> <dbl> <dbl> <int>    <chr>     <chr>
##  1 -90.6  42.5    12  2951 illinois      <NA>
##  2 -90.4  42.5    12  2952 illinois      <NA>
##  3 -89.9  42.5    12  2953 illinois      <NA>
##  4 -89.8  42.5    12  2954 illinois      <NA>
##  5 -89.4  42.5    12  2955 illinois      <NA>
##  6 -89.4  42.5    12  2956 illinois      <NA>
##  7 -88.9  42.5    12  2957 illinois      <NA>
##  8 -88.8  42.5    12  2958 illinois      <NA>
##  9 -88.7  42.5    12  2959 illinois      <NA>
## 10 -88.3  42.5    12  2960 illinois      <NA>
## # ... with 319 more rows

Now let’s get the accompanying counties:

counties <- map_data("county")
(il_county <- filter(counties, region == "illinois"))
##       long  lat group order   region   subregion
## 1    -91.5 40.2   561 21883 illinois       adams
## 2    -90.9 40.2   561 21884 illinois       adams
## 3    -90.9 40.1   561 21885 illinois       adams
## 4    -90.9 39.8   561 21886 illinois       adams
## 5    -90.9 39.8   561 21887 illinois       adams
## 6    -91.4 39.8   561 21888 illinois       adams
## 7    -91.4 39.8   561 21889 illinois       adams
## 8    -91.4 39.9   561 21890 illinois       adams
## 9    -91.5 39.9   561 21891 illinois       adams
## 10   -91.4 39.9   561 21892 illinois       adams
## 11   -91.4 39.9   561 21893 illinois       adams
## 12   -91.5 40.0   561 21894 illinois       adams
## 13   -91.5 40.0   561 21895 illinois       adams
## 14   -91.5 40.2   561 21896 illinois       adams
## 15   -91.5 40.2   561 21897 illinois       adams
## 16   -89.2 37.3   562 21899 illinois   alexander
## 17   -89.2 37.3   562 21900 illinois   alexander
## 18   -89.2 37.3   562 21901 illinois   alexander
## 19   -89.2 37.3   562 21902 illinois   alexander
## 20   -89.2 37.3   562 21903 illinois   alexander
## 21   -89.3 37.2   562 21904 illinois   alexander
## 22   -89.3 37.2   562 21905 illinois   alexander
## 23   -89.3 37.2   562 21906 illinois   alexander
## 24   -89.3 37.2   562 21907 illinois   alexander
## 25   -89.3 37.2   562 21908 illinois   alexander
## 26   -89.3 37.2   562 21909 illinois   alexander
## 27   -89.3 37.1   562 21910 illinois   alexander
## 28   -89.3 37.1   562 21911 illinois   alexander
## 29   -89.2 37.1   562 21912 illinois   alexander
## 30   -89.2 37.1   562 21913 illinois   alexander
## 31   -89.2 37.1   562 21914 illinois   alexander
## 32   -89.2 37.1   562 21915 illinois   alexander
## 33   -89.2 37.0   562 21916 illinois   alexander
## 34   -89.2 37.0   562 21917 illinois   alexander
## 35   -89.2 37.0   562 21918 illinois   alexander
## 36   -89.2 37.1   562 21919 illinois   alexander
## 37   -89.3 37.1   562 21920 illinois   alexander
## 38   -89.3 37.1   562 21921 illinois   alexander
## 39   -89.3 37.1   562 21922 illinois   alexander
## 40   -89.3 37.0   562 21923 illinois   alexander
## 41   -89.3 37.0   562 21924 illinois   alexander
## 42   -89.3 37.0   562 21925 illinois   alexander
## 43   -89.3 37.0   562 21926 illinois   alexander
## 44   -89.4 37.0   562 21927 illinois   alexander
## 45   -89.4 37.1   562 21928 illinois   alexander
## 46   -89.4 37.1   562 21929 illinois   alexander
## 47   -89.4 37.1   562 21930 illinois   alexander
## 48   -89.4 37.2   562 21931 illinois   alexander
## 49   -89.5 37.2   562 21932 illinois   alexander
## 50   -89.5 37.3   562 21933 illinois   alexander
## 51   -89.5 37.3   562 21934 illinois   alexander
## 52   -89.5 37.3   562 21935 illinois   alexander
## 53   -89.5 37.3   562 21936 illinois   alexander
## 54   -89.5 37.3   562 21937 illinois   alexander
## 55   -89.2 37.3   562 21938 illinois   alexander
## 56   -89.3 38.7   563 21940 illinois        bond
## 57   -89.6 38.7   563 21941 illinois        bond
## 58   -89.6 38.9   563 21942 illinois        bond
## 59   -89.6 38.9   563 21943 illinois        bond
## 60   -89.6 39.0   563 21944 illinois        bond
## 61   -89.6 39.0   563 21945 illinois        bond
## 62   -89.6 39.0   563 21946 illinois        bond
## 63   -89.3 39.0   563 21947 illinois        bond
## 64   -89.3 39.0   563 21948 illinois        bond
## 65   -89.3 39.0   563 21949 illinois        bond
## 66   -89.3 38.7   563 21950 illinois        bond
## 67   -88.9 42.2   564 21952 illinois       boone
## 68   -88.9 42.5   564 21953 illinois       boone
## 69   -88.8 42.5   564 21954 illinois       boone
## 70   -88.7 42.5   564 21955 illinois       boone
## 71   -88.7 42.2   564 21956 illinois       boone
## 72   -88.9 42.2   564 21957 illinois       boone
## 73   -90.9 39.8   565 21959 illinois       brown
## 74   -90.9 40.1   565 21960 illinois       brown
## 75   -90.7 40.1   565 21961 illinois       brown
## 76   -90.7 40.1   565 21962 illinois       brown
## 77   -90.7 40.0   565 21963 illinois       brown
## 78   -90.7 40.0   565 21964 illinois       brown
## 79   -90.6 40.0   565 21965 illinois       brown
## 80   -90.6 40.0   565 21966 illinois       brown
## 81   -90.6 40.0   565 21967 illinois       brown
## 82   -90.5 40.0   565 21968 illinois       brown
## 83   -90.5 40.0   565 21969 illinois       brown
## 84   -90.5 39.9   565 21970 illinois       brown
## 85   -90.6 39.9   565 21971 illinois       brown
## 86   -90.6 39.9   565 21972 illinois       brown
## 87   -90.6 39.8   565 21973 illinois       brown
## 88   -90.9 39.8   565 21974 illinois       brown
## 89   -89.6 41.6   566 21976 illinois      bureau
## 90   -89.2 41.6   566 21977 illinois      bureau
## 91   -89.2 41.3   566 21978 illinois      bureau
## 92   -89.2 41.3   566 21979 illinois      bureau
## 93   -89.3 41.3   566 21980 illinois      bureau
## 94   -89.3 41.3   566 21981 illinois      bureau
## 95   -89.3 41.3   566 21982 illinois      bureau
## 96   -89.3 41.3   566 21983 illinois      bureau
## 97   -89.4 41.2   566 21984 illinois      bureau
## 98   -89.5 41.2   566 21985 illinois      bureau
## 99   -89.5 41.1   566 21986 illinois      bureau
## 100  -89.6 41.1   566 21987 illinois      bureau
## 101  -89.6 41.2   566 21988 illinois      bureau
## 102  -89.8 41.2   566 21989 illinois      bureau
## 103  -89.8 41.5   566 21990 illinois      bureau
## 104  -89.9 41.5   566 21991 illinois      bureau
## 105  -89.9 41.6   566 21992 illinois      bureau
## 106  -89.6 41.6   566 21993 illinois      bureau
## 107  -90.9 39.4   567 21995 illinois     calhoun
## 108  -90.6 39.4   567 21996 illinois     calhoun
## 109  -90.6 39.4   567 21997 illinois     calhoun
## 110  -90.6 39.3   567 21998 illinois     calhoun
## 111  -90.6 39.3   567 21999 illinois     calhoun
## 112  -90.6 39.2   567 22000 illinois     calhoun
## 113  -90.6 39.2   567 22001 illinois     calhoun
## 114  -90.6 39.2   567 22002 illinois     calhoun
## 115  -90.6 39.1   567 22003 illinois     calhoun
## 116  -90.6 39.1   567 22004 illinois     calhoun
## 117  -90.6 39.0   567 22005 illinois     calhoun
## 118  -90.6 39.0   567 22006 illinois     calhoun
## 119  -90.6 39.0   567 22007 illinois     calhoun
## 120  -90.5 38.9   567 22008 illinois     calhoun
## 121  -90.5 39.0   567 22009 illinois     calhoun
## 122  -90.5 39.0   567 22010 illinois     calhoun
## 123  -90.5 38.9   567 22011 illinois     calhoun
## 124  -90.5 38.9   567 22012 illinois     calhoun
## 125  -90.6 38.9   567 22013 illinois     calhoun
## 126  -90.6 38.9   567 22014 illinois     calhoun
## 127  -90.6 38.9   567 22015 illinois     calhoun
## 128  -90.7 38.9   567 22016 illinois     calhoun
## 129  -90.7 38.9   567 22017 illinois     calhoun
## 130  -90.7 39.0   567 22018 illinois     calhoun
## 131  -90.7 39.1   567 22019 illinois     calhoun
## 132  -90.7 39.1   567 22020 illinois     calhoun
## 133  -90.7 39.1   567 22021 illinois     calhoun
## 134  -90.7 39.2   567 22022 illinois     calhoun
## 135  -90.7 39.2   567 22023 illinois     calhoun
## 136  -90.7 39.2   567 22024 illinois     calhoun
## 137  -90.8 39.3   567 22025 illinois     calhoun
## 138  -90.8 39.3   567 22026 illinois     calhoun
## 139  -90.9 39.4   567 22027 illinois     calhoun
## 140  -90.9 39.4   567 22028 illinois     calhoun
## 141  -89.9 42.2   568 22030 illinois     carroll
## 142  -89.7 42.2   568 22031 illinois     carroll
## 143  -89.7 41.9   568 22032 illinois     carroll
## 144  -90.2 41.9   568 22033 illinois     carroll
## 145  -90.1 42.0   568 22034 illinois     carroll
## 146  -90.1 42.0   568 22035 illinois     carroll
## 147  -90.1 42.0   568 22036 illinois     carroll
## 148  -90.2 42.1   568 22037 illinois     carroll
## 149  -90.2 42.1   568 22038 illinois     carroll
## 150  -90.2 42.2   568 22039 illinois     carroll
## 151  -90.3 42.2   568 22040 illinois     carroll
## 152  -89.9 42.2   568 22041 illinois     carroll
## 153  -90.5 40.0   569 22043 illinois        cass
## 154  -90.5 40.0   569 22044 illinois        cass
## 155  -90.4 40.0   569 22045 illinois        cass
## 156  -90.4 40.0   569 22046 illinois        cass
## 157  -90.4 40.1   569 22047 illinois        cass
## 158  -90.4 40.1   569 22048 illinois        cass
## 159  -90.4 40.1   569 22049 illinois        cass
## 160  -90.4 40.1   569 22050 illinois        cass
## 161  -90.3 40.1   569 22051 illinois        cass
## 162  -90.3 40.1   569 22052 illinois        cass
## 163  -90.3 40.1   569 22053 illinois        cass
## 164  -90.3 40.0   569 22054 illinois        cass
## 165  -90.3 40.1   569 22055 illinois        cass
## 166  -90.2 40.0   569 22056 illinois        cass
## 167  -90.2 40.1   569 22057 illinois        cass
## 168  -90.2 40.1   569 22058 illinois        cass
## 169  -90.1 40.1   569 22059 illinois        cass
## 170  -90.1 40.1   569 22060 illinois        cass
## 171  -90.1 40.1   569 22061 illinois        cass
## 172  -90.0 40.1   569 22062 illinois        cass
## 173  -90.0 40.1   569 22063 illinois        cass
## 174  -90.0 40.1   569 22064 illinois        cass
## 175  -90.0 39.9   569 22065 illinois        cass
## 176  -90.0 39.9   569 22066 illinois        cass
## 177  -90.6 39.9   569 22067 illinois        cass
## 178  -90.6 39.9   569 22068 illinois        cass
## 179  -90.5 39.9   569 22069 illinois        cass
## 180  -90.5 40.0   569 22070 illinois        cass
## 181  -90.5 40.0   569 22071 illinois        cass
## 182  -88.5 40.3   570 22073 illinois   champaign
## 183  -88.5 40.4   570 22074 illinois   champaign
## 184  -88.0 40.4   570 22075 illinois   champaign
## 185  -88.0 40.4   570 22076 illinois   champaign
## 186  -87.9 40.4   570 22077 illinois   champaign
## 187  -87.9 40.2   570 22078 illinois   champaign
## 188  -87.9 40.2   570 22079 illinois   champaign
## 189  -87.9 39.9   570 22080 illinois   champaign
## 190  -88.5 39.9   570 22081 illinois   champaign
## 191  -88.5 40.2   570 22082 illinois   champaign
## 192  -88.5 40.2   570 22083 illinois   champaign
## 193  -88.5 40.3   570 22084 illinois   champaign
## 194  -89.0 39.7   571 22086 illinois   christian
## 195  -89.0 39.4   571 22087 illinois   christian
## 196  -89.1 39.4   571 22088 illinois   christian
## 197  -89.5 39.4   571 22089 illinois   christian
## 198  -89.5 39.5   571 22090 illinois   christian
## 199  -89.5 39.6   571 22091 illinois   christian
## 200  -89.5 39.6   571 22092 illinois   christian
## 201  -89.5 39.7   571 22093 illinois   christian
## 202  -89.4 39.7   571 22094 illinois   christian
## 203  -89.4 39.8   571 22095 illinois   christian
## 204  -89.4 39.7   571 22096 illinois   christian
## 205  -89.4 39.8   571 22097 illinois   christian
## 206  -89.3 39.8   571 22098 illinois   christian
## 207  -89.3 39.8   571 22099 illinois   christian
## 208  -89.3 39.8   571 22100 illinois   christian
## 209  -89.3 39.8   571 22101 illinois   christian
## 210  -89.2 39.8   571 22102 illinois   christian
## 211  -89.2 39.8   571 22103 illinois   christian
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## 214  -89.1 39.8   571 22106 illinois   christian
## 215  -89.1 39.7   571 22107 illinois   christian
## 216  -89.0 39.7   571 22108 illinois   christian
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## 218  -88.0 39.4   572 22111 illinois       clark
## 219  -88.0 39.5   572 22112 illinois       clark
## 220  -88.0 39.5   572 22113 illinois       clark
## 221  -87.7 39.5   572 22114 illinois       clark
## 222  -87.7 39.5   572 22115 illinois       clark
## 223  -87.5 39.5   572 22116 illinois       clark
## 224  -87.5 39.4   572 22117 illinois       clark
## 225  -87.6 39.3   572 22118 illinois       clark
## 226  -87.6 39.3   572 22119 illinois       clark
## 227  -87.6 39.3   572 22120 illinois       clark
## 228  -87.6 39.3   572 22121 illinois       clark
## 229  -87.6 39.2   572 22122 illinois       clark
## 230  -87.6 39.2   572 22123 illinois       clark
## 231  -87.6 39.2   572 22124 illinois       clark
## 232  -87.6 39.2   572 22125 illinois       clark
## 233  -87.8 39.2   572 22126 illinois       clark
## 234  -87.8 39.2   572 22127 illinois       clark
## 235  -88.0 39.2   572 22128 illinois       clark
## 236  -88.0 39.2   572 22129 illinois       clark
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## 239  -88.7 38.8   573 22133 illinois        clay
## 240  -88.7 38.9   573 22134 illinois        clay
## 241  -88.4 38.9   573 22135 illinois        clay
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## 243  -88.3 38.9   573 22137 illinois        clay
## 244  -88.3 38.7   573 22138 illinois        clay
## 245  -88.3 38.7   573 22139 illinois        clay
## 246  -88.3 38.7   573 22140 illinois        clay
## 247  -88.3 38.7   573 22141 illinois        clay
## 248  -88.3 38.7   573 22142 illinois        clay
## 249  -88.3 38.6   573 22143 illinois        clay
## 250  -88.3 38.6   573 22144 illinois        clay
## 251  -88.3 38.6   573 22145 illinois        clay
## 252  -88.3 38.6   573 22146 illinois        clay
## 253  -88.3 38.6   573 22147 illinois        clay
## 254  -88.3 38.6   573 22148 illinois        clay
## 255  -88.3 38.6   573 22149 illinois        clay
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## 261  -89.1 38.7   574 22156 illinois     clinton
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## 273  -89.6 38.5   574 22168 illinois     clinton
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## 277  -89.7 38.4   574 22172 illinois     clinton
## 278  -89.7 38.4   574 22173 illinois     clinton
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## 280  -88.0 39.5   575 22176 illinois       coles
## 281  -88.0 39.5   575 22177 illinois       coles
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## 284  -88.5 39.4   575 22180 illinois       coles
## 285  -88.5 39.6   575 22181 illinois       coles
## 286  -88.1 39.7   575 22182 illinois       coles
## 287  -88.1 39.7   575 22183 illinois       coles
## 288  -88.0 39.7   575 22184 illinois       coles
## 289  -88.2 42.2   576 22186 illinois        cook
## 290  -87.8 42.2   576 22187 illinois        cook
## 291  -87.7 42.1   576 22188 illinois        cook
## 292  -87.7 42.1   576 22189 illinois        cook
## 293  -87.7 42.0   576 22190 illinois        cook
## 294  -87.6 42.0   576 22191 illinois        cook
## 295  -87.6 41.9   576 22192 illinois        cook
## 296  -87.6 41.9   576 22193 illinois        cook
## 297  -87.6 41.8   576 22194 illinois        cook
## 298  -87.6 41.8   576 22195 illinois        cook
## 299  -87.5 41.7   576 22196 illinois        cook
## 300  -87.5 41.5   576 22197 illinois        cook
## 301  -87.8 41.5   576 22198 illinois        cook
## 302  -87.8 41.6   576 22199 illinois        cook
## 303  -87.9 41.6   576 22200 illinois        cook
## 304  -87.9 41.6   576 22201 illinois        cook
## 305  -88.0 41.6   576 22202 illinois        cook
## 306  -88.0 41.7   576 22203 illinois        cook
## 307  -88.0 41.7   576 22204 illinois        cook
## 308  -88.0 41.7   576 22205 illinois        cook
## 309  -87.9 41.7   576 22206 illinois        cook
## 310  -87.9 41.7   576 22207 illinois        cook
## 311  -87.9 42.0   576 22208 illinois        cook
## 312  -88.3 42.0   576 22209 illinois        cook
## 313  -88.3 42.1   576 22210 illinois        cook
## 314  -88.2 42.1   576 22211 illinois        cook
## 315  -88.2 42.2   576 22212 illinois        cook
## 316  -88.2 42.2   576 22213 illinois        cook
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## 324  -87.6 39.2   577 22222 illinois    crawford
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## 332  -87.5 38.9   577 22230 illinois    crawford
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## 334  -88.0 39.2   578 22233 illinois  cumberland
## 335  -88.4 39.2   578 22234 illinois  cumberland
## 336  -88.5 39.2   578 22235 illinois  cumberland
## 337  -88.5 39.2   578 22236 illinois  cumberland
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## 349  -88.9 41.7   579 22249 illinois     de kalb
## 350  -88.9 41.9   579 22250 illinois     de kalb
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## 480  -90.5 40.2   589 22390 illinois      fulton
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## 491  -88.0 37.8   590 22402 illinois    gallatin
## 492  -88.1 37.7   590 22403 illinois    gallatin
## 493  -88.1 37.7   590 22404 illinois    gallatin
## 494  -88.2 37.7   590 22405 illinois    gallatin
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## 533  -88.4 38.3   593 22447 illinois    hamilton
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## 537  -88.7 38.3   593 22451 illinois    hamilton
## 538  -88.4 38.3   593 22452 illinois    hamilton
## 539  -91.2 40.6   594 22454 illinois     hancock
## 540  -90.9 40.6   594 22455 illinois     hancock
## 541  -90.9 40.3   594 22456 illinois     hancock
## 542  -90.9 40.2   594 22457 illinois     hancock
## 543  -91.5 40.2   594 22458 illinois     hancock
## 544  -91.5 40.3   594 22459 illinois     hancock
## 545  -91.5 40.3   594 22460 illinois     hancock
## 546  -91.4 40.3   594 22461 illinois     hancock
## 547  -91.4 40.4   594 22462 illinois     hancock
## 548  -91.4 40.4   594 22463 illinois     hancock
## 549  -91.4 40.4   594 22464 illinois     hancock
## 550  -91.4 40.4   594 22465 illinois     hancock
## 551  -91.4 40.4   594 22466 illinois     hancock
## 552  -91.4 40.5   594 22467 illinois     hancock
## 553  -91.4 40.6   594 22468 illinois     hancock
## 554  -91.4 40.6   594 22469 illinois     hancock
## 555  -91.4 40.6   594 22470 illinois     hancock
## 556  -91.4 40.6   594 22471 illinois     hancock
## 557  -91.3 40.6   594 22472 illinois     hancock
## 558  -91.3 40.6   594 22473 illinois     hancock
## 559  -91.2 40.6   594 22474 illinois     hancock
## 560  -88.4 37.4   595 22476 illinois      hardin
## 561  -88.4 37.4   595 22477 illinois      hardin
## 562  -88.4 37.6   595 22478 illinois      hardin
## 563  -88.4 37.6   595 22479 illinois      hardin
## 564  -88.2 37.6   595 22480 illinois      hardin
## 565  -88.1 37.6   595 22481 illinois      hardin
## 566  -88.1 37.5   595 22482 illinois      hardin
## 567  -88.1 37.5   595 22483 illinois      hardin
## 568  -88.1 37.5   595 22484 illinois      hardin
## 569  -88.1 37.5   595 22485 illinois      hardin
## 570  -88.2 37.5   595 22486 illinois      hardin
## 571  -88.3 37.5   595 22487 illinois      hardin
## 572  -88.3 37.4   595 22488 illinois      hardin
## 573  -88.4 37.4   595 22489 illinois      hardin
## 574  -90.8 41.1   596 22491 illinois   henderson
## 575  -90.8 40.6   596 22492 illinois   henderson
## 576  -90.9 40.6   596 22493 illinois   henderson
## 577  -91.2 40.6   596 22494 illinois   henderson
## 578  -91.1 40.7   596 22495 illinois   henderson
## 579  -91.1 40.7   596 22496 illinois   henderson
## 580  -91.1 40.7   596 22497 illinois   henderson
## 581  -91.1 40.8   596 22498 illinois   henderson
## 582  -91.1 40.8   596 22499 illinois   henderson
## 583  -91.0 40.9   596 22500 illinois   henderson
## 584  -91.0 40.9   596 22501 illinois   henderson
## 585  -91.0 41.0   596 22502 illinois   henderson
## 586  -90.9 41.1   596 22503 illinois   henderson
## 587  -90.8 41.1   596 22504 illinois   henderson
## 588  -90.2 41.6   597 22506 illinois       henry
## 589  -89.9 41.6   597 22507 illinois       henry
## 590  -89.9 41.5   597 22508 illinois       henry
## 591  -89.8 41.5   597 22509 illinois       henry
## 592  -89.8 41.2   597 22510 illinois       henry
## 593  -89.9 41.2   597 22511 illinois       henry
## 594  -89.9 41.1   597 22512 illinois       henry
## 595  -90.0 41.1   597 22513 illinois       henry
## 596  -90.4 41.1   597 22514 illinois       henry
## 597  -90.4 41.3   597 22515 illinois       henry
## 598  -90.4 41.5   597 22516 illinois       henry
## 599  -90.4 41.5   597 22517 illinois       henry
## 600  -90.4 41.5   597 22518 illinois       henry
## 601  -90.3 41.5   597 22519 illinois       henry
## 602  -90.2 41.5   597 22520 illinois       henry
## 603  -90.2 41.5   597 22521 illinois       henry
## 604  -90.2 41.6   597 22522 illinois       henry
## 605  -90.2 41.6   597 22523 illinois       henry
## 606  -87.5 40.7   598 22525 illinois    iroquois
## 607  -87.5 40.5   598 22526 illinois    iroquois
## 608  -87.9 40.5   598 22527 illinois    iroquois
## 609  -88.0 40.5   598 22528 illinois    iroquois
## 610  -88.0 40.5   598 22529 illinois    iroquois
## 611  -88.1 40.5   598 22530 illinois    iroquois
## 612  -88.1 40.7   598 22531 illinois    iroquois
## 613  -88.1 40.7   598 22532 illinois    iroquois
## 614  -88.1 41.0   598 22533 illinois    iroquois
## 615  -87.5 41.0   598 22534 illinois    iroquois
## 616  -87.5 40.7   598 22535 illinois    iroquois
## 617  -89.7 37.8   599 22537 illinois     jackson
## 618  -89.7 37.8   599 22538 illinois     jackson
## 619  -89.7 37.8   599 22539 illinois     jackson
## 620  -89.7 37.9   599 22540 illinois     jackson
## 621  -89.6 37.9   599 22541 illinois     jackson
## 622  -89.6 38.0   599 22542 illinois     jackson
## 623  -89.2 38.0   599 22543 illinois     jackson
## 624  -89.2 38.0   599 22544 illinois     jackson
## 625  -89.2 37.9   599 22545 illinois     jackson
## 626  -89.2 37.6   599 22546 illinois     jackson
## 627  -89.4 37.6   599 22547 illinois     jackson
## 628  -89.5 37.6   599 22548 illinois     jackson
## 629  -89.5 37.6   599 22549 illinois     jackson
## 630  -89.5 37.6   599 22550 illinois     jackson
## 631  -89.5 37.6   599 22551 illinois     jackson
## 632  -89.5 37.6   599 22552 illinois     jackson
## 633  -89.5 37.7   599 22553 illinois     jackson
## 634  -89.5 37.7   599 22554 illinois     jackson
## 635  -89.6 37.7   599 22555 illinois     jackson
## 636  -89.7 37.8   599 22556 illinois     jackson
## 637  -89.7 37.8   599 22557 illinois     jackson
## 638  -88.4 39.2   600 22559 illinois      jasper
## 639  -88.0 39.2   600 22560 illinois      jasper
## 640  -88.0 39.2   600 22561 illinois      jasper
## 641  -88.0 38.9   600 22562 illinois      jasper
## 642  -88.3 38.9   600 22563 illinois      jasper
## 643  -88.4 38.9   600 22564 illinois      jasper
## 644  -88.4 38.9   600 22565 illinois      jasper
## 645  -88.4 39.2   600 22566 illinois      jasper
## 646  -89.1 38.2   601 22568 illinois   jefferson
## 647  -89.1 38.5   601 22569 illinois   jefferson
## 648  -88.7 38.5   601 22570 illinois   jefferson
## 649  -88.7 38.3   601 22571 illinois   jefferson
## 650  -88.7 38.1   601 22572 illinois   jefferson
## 651  -89.1 38.1   601 22573 illinois   jefferson
## 652  -89.1 38.1   601 22574 illinois   jefferson
## 653  -89.1 38.2   601 22575 illinois   jefferson
## 654  -90.5 39.0   602 22577 illinois      jersey
## 655  -90.5 39.0   602 22578 illinois      jersey
## 656  -90.5 38.9   602 22579 illinois      jersey
## 657  -90.6 39.0   602 22580 illinois      jersey
## 658  -90.6 39.0   602 22581 illinois      jersey
## 659  -90.6 39.0   602 22582 illinois      jersey
## 660  -90.6 39.1   602 22583 illinois      jersey
## 661  -90.6 39.1   602 22584 illinois      jersey
## 662  -90.6 39.2   602 22585 illinois      jersey
## 663  -90.6 39.2   602 22586 illinois      jersey
## 664  -90.6 39.2   602 22587 illinois      jersey
## 665  -90.5 39.2   602 22588 illinois      jersey
## 666  -90.5 39.2   602 22589 illinois      jersey
## 667  -90.3 39.2   602 22590 illinois      jersey
## 668  -90.3 39.2   602 22591 illinois      jersey
## 669  -90.2 39.2   602 22592 illinois      jersey
## 670  -90.2 39.3   602 22593 illinois      jersey
## 671  -90.1 39.3   602 22594 illinois      jersey
## 672  -90.1 39.0   602 22595 illinois      jersey
## 673  -90.3 39.0   602 22596 illinois      jersey
## 674  -90.3 38.9   602 22597 illinois      jersey
## 675  -90.3 38.9   602 22598 illinois      jersey
## 676  -90.4 38.9   602 22599 illinois      jersey
## 677  -90.4 38.9   602 22600 illinois      jersey
## 678  -90.5 39.0   602 22601 illinois      jersey
## 679  -89.9 42.5   603 22603 illinois  jo daviess
## 680  -89.9 42.2   603 22604 illinois  jo daviess
## 681  -90.3 42.2   603 22605 illinois  jo daviess
## 682  -90.4 42.2   603 22606 illinois  jo daviess
## 683  -90.4 42.2   603 22607 illinois  jo daviess
## 684  -90.4 42.3   603 22608 illinois  jo daviess
## 685  -90.4 42.3   603 22609 illinois  jo daviess
## 686  -90.5 42.4   603 22610 illinois  jo daviess
## 687  -90.5 42.4   603 22611 illinois  jo daviess
## 688  -90.6 42.5   603 22612 illinois  jo daviess
## 689  -90.6 42.5   603 22613 illinois  jo daviess
## 690  -90.6 42.5   603 22614 illinois  jo daviess
## 691  -90.4 42.5   603 22615 illinois  jo daviess
## 692  -89.9 42.5   603 22616 illinois  jo daviess
## 693  -89.0 37.6   604 22618 illinois     johnson
## 694  -88.7 37.6   604 22619 illinois     johnson
## 695  -88.7 37.3   604 22620 illinois     johnson
## 696  -88.9 37.3   604 22621 illinois     johnson
## 697  -88.9 37.3   604 22622 illinois     johnson
## 698  -88.9 37.3   604 22623 illinois     johnson
## 699  -88.9 37.3   604 22624 illinois     johnson
## 700  -88.9 37.3   604 22625 illinois     johnson
## 701  -89.0 37.3   604 22626 illinois     johnson
## 702  -89.0 37.3   604 22627 illinois     johnson
## 703  -89.0 37.3   604 22628 illinois     johnson
## 704  -89.0 37.3   604 22629 illinois     johnson
## 705  -89.0 37.3   604 22630 illinois     johnson
## 706  -89.0 37.3   604 22631 illinois     johnson
## 707  -89.0 37.6   604 22632 illinois     johnson
## 708  -88.6 42.2   605 22634 illinois        kane
## 709  -88.2 42.2   605 22635 illinois        kane
## 710  -88.2 42.1   605 22636 illinois        kane
## 711  -88.3 42.1   605 22637 illinois        kane
## 712  -88.3 42.0   605 22638 illinois        kane
## 713  -88.3 41.7   605 22639 illinois        kane
## 714  -88.6 41.7   605 22640 illinois        kane
## 715  -88.6 42.1   605 22641 illinois        kane
## 716  -88.6 42.1   605 22642 illinois        kane
## 717  -88.6 42.2   605 22643 illinois        kane
## 718  -88.2 41.0   606 22645 illinois    kankakee
## 719  -88.2 41.1   606 22646 illinois    kankakee
## 720  -88.2 41.2   606 22647 illinois    kankakee
## 721  -88.0 41.2   606 22648 illinois    kankakee
## 722  -88.0 41.3   606 22649 illinois    kankakee
## 723  -87.5 41.3   606 22650 illinois    kankakee
## 724  -87.5 41.2   606 22651 illinois    kankakee
## 725  -87.5 41.0   606 22652 illinois    kankakee
## 726  -88.1 41.0   606 22653 illinois    kankakee
## 727  -88.2 41.0   606 22654 illinois    kankakee
## 728  -88.3 41.7   607 22656 illinois     kendall
## 729  -88.3 41.5   607 22657 illinois     kendall
## 730  -88.6 41.5   607 22658 illinois     kendall
## 731  -88.6 41.6   607 22659 illinois     kendall
## 732  -88.6 41.7   607 22660 illinois     kendall
## 733  -88.3 41.7   607 22661 illinois     kendall
## 734  -90.4 41.1   608 22663 illinois        knox
## 735  -90.0 41.1   608 22664 illinois        knox
## 736  -90.0 41.0   608 22665 illinois        knox
## 737  -90.0 40.7   608 22666 illinois        knox
## 738  -90.4 40.7   608 22667 illinois        knox
## 739  -90.4 41.1   608 22668 illinois        knox
## 740  -90.4 41.1   608 22669 illinois        knox
## 741  -87.8 42.2   609 22671 illinois        lake
## 742  -88.2 42.2   609 22672 illinois        lake
## 743  -88.2 42.5   609 22673 illinois        lake
## 744  -87.8 42.5   609 22674 illinois        lake
## 745  -87.8 42.5   609 22675 illinois        lake
## 746  -87.8 42.4   609 22676 illinois        lake
## 747  -87.8 42.4   609 22677 illinois        lake
## 748  -87.8 42.3   609 22678 illinois        lake
## 749  -87.8 42.3   609 22679 illinois        lake
## 750  -87.8 42.2   609 22680 illinois        lake
## 751  -87.8 42.2   609 22681 illinois        lake
## 752  -87.8 42.2   609 22682 illinois        lake
## 753  -88.6 41.6   610 22684 illinois    la salle
## 754  -88.6 41.5   610 22685 illinois    la salle
## 755  -88.6 41.1   610 22686 illinois    la salle
## 756  -88.9 41.1   610 22687 illinois    la salle
## 757  -88.9 40.9   610 22688 illinois    la salle
## 758  -89.0 40.9   610 22689 illinois    la salle
## 759  -89.0 41.1   610 22690 illinois    la salle
## 760  -89.2 41.1   610 22691 illinois    la salle
## 761  -89.2 41.3   610 22692 illinois    la salle
## 762  -89.2 41.6   610 22693 illinois    la salle
## 763  -89.2 41.6   610 22694 illinois    la salle
## 764  -88.9 41.6   610 22695 illinois    la salle
## 765  -88.6 41.6   610 22696 illinois    la salle
## 766  -87.5 38.9   611 22698 illinois    lawrence
## 767  -87.5 38.8   611 22699 illinois    lawrence
## 768  -87.5 38.7   611 22700 illinois    lawrence
## 769  -87.5 38.7   611 22701 illinois    lawrence
## 770  -87.5 38.7   611 22702 illinois    lawrence
## 771  -87.6 38.7   611 22703 illinois    lawrence
## 772  -87.6 38.6   611 22704 illinois    lawrence
## 773  -87.6 38.6   611 22705 illinois    lawrence
## 774  -87.7 38.6   611 22706 illinois    lawrence
## 775  -87.9 38.6   611 22707 illinois    lawrence
## 776  -87.9 38.9   611 22708 illinois    lawrence
## 777  -87.5 38.9   611 22709 illinois    lawrence
## 778  -88.9 41.9   612 22711 illinois         lee
## 779  -88.9 41.7   612 22712 illinois         lee
## 780  -88.9 41.7   612 22713 illinois         lee
## 781  -88.9 41.6   612 22714 illinois         lee
## 782  -89.2 41.6   612 22715 illinois         lee
## 783  -89.2 41.6   612 22716 illinois         lee
## 784  -89.6 41.6   612 22717 illinois         lee
## 785  -89.6 41.9   612 22718 illinois         lee
## 786  -89.5 41.9   612 22719 illinois         lee
## 787  -89.5 41.9   612 22720 illinois         lee
## 788  -89.4 41.9   612 22721 illinois         lee
## 789  -89.4 41.9   612 22722 illinois         lee
## 790  -89.4 41.9   612 22723 illinois         lee
## 791  -89.4 41.9   612 22724 illinois         lee
## 792  -89.4 41.9   612 22725 illinois         lee
## 793  -89.4 41.9   612 22726 illinois         lee
## 794  -88.9 41.9   612 22727 illinois         lee
## 795  -88.9 40.7   613 22729 illinois  livingston
## 796  -88.9 40.9   613 22730 illinois  livingston
## 797  -88.9 41.1   613 22731 illinois  livingston
## 798  -88.6 41.1   613 22732 illinois  livingston
## 799  -88.2 41.1   613 22733 illinois  livingston
## 800  -88.2 41.0   613 22734 illinois  livingston
## 801  -88.2 40.6   613 22735 illinois  livingston
## 802  -88.5 40.6   613 22736 illinois  livingston
## 803  -88.6 40.6   613 22737 illinois  livingston
## 804  -88.6 40.7   613 22738 illinois  livingston
## 805  -88.6 40.7   613 22739 illinois  livingston
## 806  -88.6 40.7   613 22740 illinois  livingston
## 807  -88.9 40.7   613 22741 illinois  livingston
## 808  -89.6 40.3   614 22743 illinois       logan
## 809  -89.3 40.3   614 22744 illinois       logan
## 810  -89.3 40.3   614 22745 illinois       logan
## 811  -89.1 40.3   614 22746 illinois       logan
## 812  -89.1 40.0   614 22747 illinois       logan
## 813  -89.1 39.9   614 22748 illinois       logan
## 814  -89.2 39.9   614 22749 illinois       logan
## 815  -89.4 39.9   614 22750 illinois       logan
## 816  -89.4 39.9   614 22751 illinois       logan
## 817  -89.5 39.9   614 22752 illinois       logan
## 818  -89.5 40.0   614 22753 illinois       logan
## 819  -89.6 40.0   614 22754 illinois       logan
## 820  -89.6 40.1   614 22755 illinois       logan
## 821  -89.6 40.1   614 22756 illinois       logan
## 822  -89.6 40.1   614 22757 illinois       logan
## 823  -89.6 40.3   614 22758 illinois       logan
## 824  -90.8 40.6   615 22760 illinois   mcdonough
## 825  -90.4 40.6   615 22761 illinois   mcdonough
## 826  -90.5 40.3   615 22762 illinois   mcdonough
## 827  -90.9 40.3   615 22763 illinois   mcdonough
## 828  -90.9 40.6   615 22764 illinois   mcdonough
## 829  -90.8 40.6   615 22765 illinois   mcdonough
## 830  -88.2 42.5   616 22767 illinois     mchenry
## 831  -88.2 42.2   616 22768 illinois     mchenry
## 832  -88.2 42.2   616 22769 illinois     mchenry
## 833  -88.6 42.2   616 22770 illinois     mchenry
## 834  -88.7 42.2   616 22771 illinois     mchenry
## 835  -88.7 42.5   616 22772 illinois     mchenry
## 836  -88.3 42.5   616 22773 illinois     mchenry
## 837  -88.2 42.5   616 22774 illinois     mchenry
## 838  -88.5 40.6   617 22776 illinois      mclean
## 839  -88.5 40.4   617 22777 illinois      mclean
## 840  -88.5 40.3   617 22778 illinois      mclean
## 841  -88.6 40.3   617 22779 illinois      mclean
## 842  -89.1 40.3   617 22780 illinois      mclean
## 843  -89.3 40.3   617 22781 illinois      mclean
## 844  -89.3 40.3   617 22782 illinois      mclean
## 845  -89.3 40.6   617 22783 illinois      mclean
## 846  -89.1 40.6   617 22784 illinois      mclean
## 847  -89.1 40.6   617 22785 illinois      mclean
## 848  -89.1 40.6   617 22786 illinois      mclean
## 849  -89.1 40.6   617 22787 illinois      mclean
## 850  -89.0 40.6   617 22788 illinois      mclean
## 851  -89.0 40.7   617 22789 illinois      mclean
## 852  -89.0 40.7   617 22790 illinois      mclean
## 853  -89.0 40.7   617 22791 illinois      mclean
## 854  -88.9 40.7   617 22792 illinois      mclean
## 855  -88.6 40.7   617 22793 illinois      mclean
## 856  -88.6 40.7   617 22794 illinois      mclean
## 857  -88.6 40.7   617 22795 illinois      mclean
## 858  -88.6 40.6   617 22796 illinois      mclean
## 859  -88.5 40.6   617 22797 illinois      mclean
## 860  -89.1 40.0   618 22799 illinois       macon
## 861  -88.7 40.1   618 22800 illinois       macon
## 862  -88.8 39.8   618 22801 illinois       macon
## 863  -88.8 39.8   618 22802 illinois       macon
## 864  -88.8 39.7   618 22803 illinois       macon
## 865  -88.8 39.7   618 22804 illinois       macon
## 866  -88.8 39.6   618 22805 illinois       macon
## 867  -89.0 39.7   618 22806 illinois       macon
## 868  -89.1 39.7   618 22807 illinois       macon
## 869  -89.1 39.8   618 22808 illinois       macon
## 870  -89.1 39.8   618 22809 illinois       macon
## 871  -89.2 39.8   618 22810 illinois       macon
## 872  -89.2 39.8   618 22811 illinois       macon
## 873  -89.2 39.9   618 22812 illinois       macon
## 874  -89.1 39.9   618 22813 illinois       macon
## 875  -89.1 40.0   618 22814 illinois       macon
## 876  -89.9 39.5   619 22816 illinois    macoupin
## 877  -89.7 39.5   619 22817 illinois    macoupin
## 878  -89.7 39.4   619 22818 illinois    macoupin
## 879  -89.7 39.0   619 22819 illinois    macoupin
## 880  -90.1 39.0   619 22820 illinois    macoupin
## 881  -90.1 39.3   619 22821 illinois    macoupin
## 882  -90.1 39.4   619 22822 illinois    macoupin
## 883  -90.2 39.4   619 22823 illinois    macoupin
## 884  -90.1 39.5   619 22824 illinois    macoupin
## 885  -89.9 39.5   619 22825 illinois    macoupin
## 886  -90.3 38.9   620 22827 illinois     madison
## 887  -90.3 39.0   620 22828 illinois     madison
## 888  -90.1 39.0   620 22829 illinois     madison
## 889  -89.7 39.0   620 22830 illinois     madison
## 890  -89.6 39.0   620 22831 illinois     madison
## 891  -89.6 38.9   620 22832 illinois     madison
## 892  -89.6 38.9   620 22833 illinois     madison
## 893  -89.6 38.7   620 22834 illinois     madison
## 894  -89.6 38.7   620 22835 illinois     madison
## 895  -89.7 38.7   620 22836 illinois     madison
## 896  -90.2 38.7   620 22837 illinois     madison
## 897  -90.2 38.7   620 22838 illinois     madison
## 898  -90.2 38.7   620 22839 illinois     madison
## 899  -90.2 38.8   620 22840 illinois     madison
## 900  -90.1 38.8   620 22841 illinois     madison
## 901  -90.1 38.8   620 22842 illinois     madison
## 902  -90.1 38.8   620 22843 illinois     madison
## 903  -90.1 38.9   620 22844 illinois     madison
## 904  -90.2 38.9   620 22845 illinois     madison
## 905  -90.2 38.9   620 22846 illinois     madison
## 906  -90.3 38.9   620 22847 illinois     madison
## 907  -89.1 38.5   621 22849 illinois      marion
## 908  -89.1 38.7   621 22850 illinois      marion
## 909  -89.1 38.8   621 22851 illinois      marion
## 910  -88.7 38.8   621 22852 illinois      marion
## 911  -88.7 38.6   621 22853 illinois      marion
## 912  -88.7 38.5   621 22854 illinois      marion
## 913  -89.1 38.5   621 22855 illinois      marion
## 914  -89.1 38.5   621 22856 illinois      marion
## 915  -89.6 41.0   622 22858 illinois    marshall
## 916  -89.6 41.1   622 22859 illinois    marshall
## 917  -89.5 41.1   622 22860 illinois    marshall
## 918  -89.3 41.1   622 22861 illinois    marshall
## 919  -89.3 41.1   622 22862 illinois    marshall
## 920  -89.2 41.1   622 22863 illinois    marshall
## 921  -89.0 41.1   622 22864 illinois    marshall
## 922  -89.0 40.9   622 22865 illinois    marshall
## 923  -89.5 40.9   622 22866 illinois    marshall
## 924  -89.4 41.0   622 22867 illinois    marshall
## 925  -89.6 41.0   622 22868 illinois    marshall
## 926  -90.4 40.1   623 22870 illinois       mason
## 927  -90.3 40.1   623 22871 illinois       mason
## 928  -90.3 40.1   623 22872 illinois       mason
## 929  -90.3 40.1   623 22873 illinois       mason
## 930  -90.3 40.1   623 22874 illinois       mason
## 931  -90.2 40.1   623 22875 illinois       mason
## 932  -90.2 40.2   623 22876 illinois       mason
## 933  -90.2 40.2   623 22877 illinois       mason
## 934  -90.2 40.2   623 22878 illinois       mason
## 935  -90.1 40.2   623 22879 illinois       mason
## 936  -90.1 40.3   623 22880 illinois       mason
## 937  -90.1 40.3   623 22881 illinois       mason
## 938  -90.1 40.3   623 22882 illinois       mason
## 939  -90.0 40.4   623 22883 illinois       mason
## 940  -90.0 40.4   623 22884 illinois       mason
## 941  -89.9 40.4   623 22885 illinois       mason
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## 1319 -90.2 41.6   641 23281 illinois rock island
## 1320 -90.2 41.5   641 23282 illinois rock island
## 1321 -90.2 41.5   641 23283 illinois rock island
## 1322 -90.3 41.5   641 23284 illinois rock island
## 1323 -90.4 41.5   641 23285 illinois rock island
## 1324 -90.4 41.5   641 23286 illinois rock island
## 1325 -90.4 41.5   641 23287 illinois rock island
## 1326 -90.4 41.3   641 23288 illinois rock island
## 1327 -91.1 41.3   641 23289 illinois rock island
## 1328 -91.1 41.3   641 23290 illinois rock island
## 1329 -91.0 41.4   641 23291 illinois rock island
## 1330 -91.0 41.4   641 23292 illinois rock island
## 1331 -91.0 41.4   641 23293 illinois rock island
## 1332 -90.9 41.4   641 23294 illinois rock island
## 1333 -90.9 41.4   641 23295 illinois rock island
## 1334 -90.8 41.5   641 23296 illinois rock island
## 1335 -90.8 41.5   641 23297 illinois rock island
## 1336 -90.7 41.5   641 23298 illinois rock island
## 1337 -90.7 41.5   641 23299 illinois rock island
## 1338 -90.6 41.5   641 23300 illinois rock island
## 1339 -90.5 41.5   641 23301 illinois rock island
## 1340 -90.5 41.5   641 23302 illinois rock island
## 1341 -90.4 41.6   641 23303 illinois rock island
## 1342 -90.4 41.6   641 23304 illinois rock island
## 1343 -90.3 41.6   641 23305 illinois rock island
## 1344 -90.3 41.7   641 23306 illinois rock island
## 1345 -90.3 41.7   641 23307 illinois rock island
## 1346 -90.3 41.8   641 23308 illinois rock island
## 1347 -90.3 41.8   641 23309 illinois rock island
## 1348 -90.2 41.8   641 23310 illinois rock island
## 1349 -90.2 38.7   642 23312 illinois    st clair
## 1350 -89.7 38.7   642 23313 illinois    st clair
## 1351 -89.7 38.4   642 23314 illinois    st clair
## 1352 -89.7 38.2   642 23315 illinois    st clair
## 1353 -89.9 38.2   642 23316 illinois    st clair
## 1354 -89.9 38.3   642 23317 illinois    st clair
## 1355 -89.9 38.3   642 23318 illinois    st clair
## 1356 -89.9 38.3   642 23319 illinois    st clair
## 1357 -90.1 38.3   642 23320 illinois    st clair
## 1358 -90.1 38.3   642 23321 illinois    st clair
## 1359 -90.1 38.4   642 23322 illinois    st clair
## 1360 -90.1 38.4   642 23323 illinois    st clair
## 1361 -90.2 38.4   642 23324 illinois    st clair
## 1362 -90.1 38.4   642 23325 illinois    st clair
## 1363 -90.3 38.5   642 23326 illinois    st clair
## 1364 -90.2 38.5   642 23327 illinois    st clair
## 1365 -90.2 38.6   642 23328 illinois    st clair
## 1366 -90.2 38.6   642 23329 illinois    st clair
## 1367 -90.2 38.7   642 23330 illinois    st clair
## 1368 -88.4 37.6   643 23332 illinois      saline
## 1369 -88.7 37.6   643 23333 illinois      saline
## 1370 -88.7 37.9   643 23334 illinois      saline
## 1371 -88.7 37.9   643 23335 illinois      saline
## 1372 -88.4 37.9   643 23336 illinois      saline
## 1373 -88.4 37.6   643 23337 illinois      saline
## 1374 -88.4 37.6   643 23338 illinois      saline
## 1375 -89.2 39.8   644 23340 illinois    sangamon
## 1376 -89.2 39.8   644 23341 illinois    sangamon
## 1377 -89.3 39.8   644 23342 illinois    sangamon
## 1378 -89.3 39.8   644 23343 illinois    sangamon
## 1379 -89.3 39.8   644 23344 illinois    sangamon
## 1380 -89.3 39.8   644 23345 illinois    sangamon
## 1381 -89.4 39.8   644 23346 illinois    sangamon
## 1382 -89.4 39.7   644 23347 illinois    sangamon
## 1383 -89.4 39.8   644 23348 illinois    sangamon
## 1384 -89.4 39.7   644 23349 illinois    sangamon
## 1385 -89.5 39.7   644 23350 illinois    sangamon
## 1386 -89.5 39.6   644 23351 illinois    sangamon
## 1387 -89.5 39.6   644 23352 illinois    sangamon
## 1388 -89.5 39.5   644 23353 illinois    sangamon
## 1389 -89.7 39.5   644 23354 illinois    sangamon
## 1390 -89.9 39.5   644 23355 illinois    sangamon
## 1391 -89.9 39.6   644 23356 illinois    sangamon
## 1392 -90.0 39.7   644 23357 illinois    sangamon
## 1393 -90.0 39.8   644 23358 illinois    sangamon
## 1394 -90.0 39.8   644 23359 illinois    sangamon
## 1395 -90.0 39.9   644 23360 illinois    sangamon
## 1396 -90.0 39.9   644 23361 illinois    sangamon
## 1397 -89.8 39.9   644 23362 illinois    sangamon
## 1398 -89.8 39.9   644 23363 illinois    sangamon
## 1399 -89.7 39.9   644 23364 illinois    sangamon
## 1400 -89.7 40.0   644 23365 illinois    sangamon
## 1401 -89.7 40.0   644 23366 illinois    sangamon
## 1402 -89.7 40.0   644 23367 illinois    sangamon
## 1403 -89.6 40.0   644 23368 illinois    sangamon
## 1404 -89.5 40.0   644 23369 illinois    sangamon
## 1405 -89.5 39.9   644 23370 illinois    sangamon
## 1406 -89.4 39.9   644 23371 illinois    sangamon
## 1407 -89.4 39.9   644 23372 illinois    sangamon
## 1408 -89.2 39.9   644 23373 illinois    sangamon
## 1409 -89.2 39.8   644 23374 illinois    sangamon
## 1410 -90.9 40.3   645 23376 illinois    schuyler
## 1411 -90.5 40.3   645 23377 illinois    schuyler
## 1412 -90.5 40.2   645 23378 illinois    schuyler
## 1413 -90.2 40.2   645 23379 illinois    schuyler
## 1414 -90.2 40.2   645 23380 illinois    schuyler
## 1415 -90.2 40.1   645 23381 illinois    schuyler
## 1416 -90.3 40.1   645 23382 illinois    schuyler
## 1417 -90.3 40.1   645 23383 illinois    schuyler
## 1418 -90.3 40.1   645 23384 illinois    schuyler
## 1419 -90.3 40.1   645 23385 illinois    schuyler
## 1420 -90.4 40.1   645 23386 illinois    schuyler
## 1421 -90.4 40.1   645 23387 illinois    schuyler
## 1422 -90.4 40.1   645 23388 illinois    schuyler
## 1423 -90.4 40.1   645 23389 illinois    schuyler
## 1424 -90.4 40.0   645 23390 illinois    schuyler
## 1425 -90.4 40.0   645 23391 illinois    schuyler
## 1426 -90.5 40.0   645 23392 illinois    schuyler
## 1427 -90.5 40.0   645 23393 illinois    schuyler
## 1428 -90.6 40.0   645 23394 illinois    schuyler
## 1429 -90.6 40.0   645 23395 illinois    schuyler
## 1430 -90.6 40.0   645 23396 illinois    schuyler
## 1431 -90.7 40.0   645 23397 illinois    schuyler
## 1432 -90.7 40.0   645 23398 illinois    schuyler
## 1433 -90.7 40.1   645 23399 illinois    schuyler
## 1434 -90.7 40.1   645 23400 illinois    schuyler
## 1435 -90.9 40.1   645 23401 illinois    schuyler
## 1436 -90.9 40.2   645 23402 illinois    schuyler
## 1437 -90.9 40.3   645 23403 illinois    schuyler
## 1438 -90.6 39.8   646 23405 illinois       scott
## 1439 -90.5 39.8   646 23406 illinois       scott
## 1440 -90.5 39.8   646 23407 illinois       scott
## 1441 -90.4 39.7   646 23408 illinois       scott
## 1442 -90.4 39.7   646 23409 illinois       scott
## 1443 -90.3 39.7   646 23410 illinois       scott
## 1444 -90.3 39.6   646 23411 illinois       scott
## 1445 -90.3 39.6   646 23412 illinois       scott
## 1446 -90.3 39.5   646 23413 illinois       scott
## 1447 -90.6 39.5   646 23414 illinois       scott
## 1448 -90.6 39.6   646 23415 illinois       scott
## 1449 -90.7 39.7   646 23416 illinois       scott
## 1450 -90.7 39.7   646 23417 illinois       scott
## 1451 -90.6 39.7   646 23418 illinois       scott
## 1452 -90.6 39.8   646 23419 illinois       scott
## 1453 -88.8 39.6   647 23421 illinois      shelby
## 1454 -88.8 39.6   647 23422 illinois      shelby
## 1455 -88.7 39.6   647 23423 illinois      shelby
## 1456 -88.7 39.5   647 23424 illinois      shelby
## 1457 -88.6 39.5   647 23425 illinois      shelby
## 1458 -88.6 39.5   647 23426 illinois      shelby
## 1459 -88.6 39.5   647 23427 illinois      shelby
## 1460 -88.6 39.5   647 23428 illinois      shelby
## 1461 -88.6 39.5   647 23429 illinois      shelby
## 1462 -88.6 39.5   647 23430 illinois      shelby
## 1463 -88.6 39.5   647 23431 illinois      shelby
## 1464 -88.6 39.4   647 23432 illinois      shelby
## 1465 -88.5 39.4   647 23433 illinois      shelby
## 1466 -88.5 39.4   647 23434 illinois      shelby
## 1467 -88.5 39.2   647 23435 illinois      shelby
## 1468 -88.8 39.2   647 23436 illinois      shelby
## 1469 -89.1 39.2   647 23437 illinois      shelby
## 1470 -89.1 39.4   647 23438 illinois      shelby
## 1471 -89.0 39.4   647 23439 illinois      shelby
## 1472 -89.0 39.7   647 23440 illinois      shelby
## 1473 -88.8 39.6   647 23441 illinois      shelby
## 1474 -90.0 41.0   648 23443 illinois       stark
## 1475 -90.0 41.1   648 23444 illinois       stark
## 1476 -89.9 41.1   648 23445 illinois       stark
## 1477 -89.9 41.2   648 23446 illinois       stark
## 1478 -89.8 41.2   648 23447 illinois       stark
## 1479 -89.6 41.2   648 23448 illinois       stark
## 1480 -89.6 41.1   648 23449 illinois       stark
## 1481 -89.6 41.0   648 23450 illinois       stark
## 1482 -90.0 41.0   648 23451 illinois       stark
## 1483 -89.9 42.2   649 23453 illinois  stephenson
## 1484 -89.9 42.5   649 23454 illinois  stephenson
## 1485 -89.8 42.5   649 23455 illinois  stephenson
## 1486 -89.4 42.5   649 23456 illinois  stephenson
## 1487 -89.4 42.2   649 23457 illinois  stephenson
## 1488 -89.7 42.2   649 23458 illinois  stephenson
## 1489 -89.9 42.2   649 23459 illinois  stephenson
## 1490 -89.3 40.6   650 23461 illinois    tazewell
## 1491 -89.3 40.3   650 23462 illinois    tazewell
## 1492 -89.6 40.3   650 23463 illinois    tazewell
## 1493 -89.7 40.3   650 23464 illinois    tazewell
## 1494 -89.7 40.4   650 23465 illinois    tazewell
## 1495 -89.9 40.4   650 23466 illinois    tazewell
## 1496 -89.9 40.5   650 23467 illinois    tazewell
## 1497 -89.9 40.5   650 23468 illinois    tazewell
## 1498 -89.9 40.5   650 23469 illinois    tazewell
## 1499 -89.8 40.5   650 23470 illinois    tazewell
## 1500 -89.8 40.5   650 23471 illinois    tazewell
## 1501 -89.7 40.5   650 23472 illinois    tazewell
## 1502 -89.7 40.6   650 23473 illinois    tazewell
## 1503 -89.7 40.5   650 23474 illinois    tazewell
## 1504 -89.7 40.6   650 23475 illinois    tazewell
## 1505 -89.7 40.6   650 23476 illinois    tazewell
## 1506 -89.6 40.6   650 23477 illinois    tazewell
## 1507 -89.6 40.7   650 23478 illinois    tazewell
## 1508 -89.6 40.7   650 23479 illinois    tazewell
## 1509 -89.6 40.7   650 23480 illinois    tazewell
## 1510 -89.6 40.8   650 23481 illinois    tazewell
## 1511 -89.3 40.7   650 23482 illinois    tazewell
## 1512 -89.3 40.6   650 23483 illinois    tazewell
## 1513 -89.3 40.6   650 23484 illinois    tazewell
## 1514 -89.3 40.6   650 23485 illinois    tazewell
## 1515 -89.5 37.6   651 23487 illinois       union
## 1516 -89.5 37.6   651 23488 illinois       union
## 1517 -89.5 37.6   651 23489 illinois       union
## 1518 -89.5 37.6   651 23490 illinois       union
## 1519 -89.5 37.6   651 23491 illinois       union
## 1520 -89.4 37.6   651 23492 illinois       union
## 1521 -89.2 37.6   651 23493 illinois       union
## 1522 -89.0 37.6   651 23494 illinois       union
## 1523 -89.0 37.3   651 23495 illinois       union
## 1524 -89.2 37.3   651 23496 illinois       union
## 1525 -89.5 37.3   651 23497 illinois       union
## 1526 -89.4 37.4   651 23498 illinois       union
## 1527 -89.4 37.4   651 23499 illinois       union
## 1528 -89.4 37.4   651 23500 illinois       union
## 1529 -89.5 37.5   651 23501 illinois       union
## 1530 -89.5 37.6   651 23502 illinois       union
## 1531 -87.9 40.4   652 23504 illinois   vermilion
## 1532 -87.9 40.5   652 23505 illinois   vermilion
## 1533 -87.5 40.5   652 23506 illinois   vermilion
## 1534 -87.5 40.5   652 23507 illinois   vermilion
## 1535 -87.5 40.1   652 23508 illinois   vermilion
## 1536 -87.5 39.9   652 23509 illinois   vermilion
## 1537 -87.6 39.9   652 23510 illinois   vermilion
## 1538 -87.6 39.9   652 23511 illinois   vermilion
## 1539 -87.9 39.9   652 23512 illinois   vermilion
## 1540 -87.9 40.2   652 23513 illinois   vermilion
## 1541 -87.9 40.2   652 23514 illinois   vermilion
## 1542 -87.9 40.4   652 23515 illinois   vermilion
## 1543 -88.0 38.6   653 23517 illinois      wabash
## 1544 -87.9 38.6   653 23518 illinois      wabash
## 1545 -87.7 38.6   653 23519 illinois      wabash
## 1546 -87.7 38.5   653 23520 illinois      wabash
## 1547 -87.7 38.5   653 23521 illinois      wabash
## 1548 -87.7 38.5   653 23522 illinois      wabash
## 1549 -87.7 38.5   653 23523 illinois      wabash
## 1550 -87.7 38.4   653 23524 illinois      wabash
## 1551 -87.8 38.4   653 23525 illinois      wabash
## 1552 -87.8 38.4   653 23526 illinois      wabash
## 1553 -87.8 38.3   653 23527 illinois      wabash
## 1554 -87.8 38.3   653 23528 illinois      wabash
## 1555 -87.8 38.3   653 23529 illinois      wabash
## 1556 -87.9 38.3   653 23530 illinois      wabash
## 1557 -87.9 38.3   653 23531 illinois      wabash
## 1558 -87.9 38.3   653 23532 illinois      wabash
## 1559 -87.9 38.3   653 23533 illinois      wabash
## 1560 -88.0 38.3   653 23534 illinois      wabash
## 1561 -88.0 38.4   653 23535 illinois      wabash
## 1562 -87.9 38.4   653 23536 illinois      wabash
## 1563 -87.9 38.5   653 23537 illinois      wabash
## 1564 -87.9 38.5   653 23538 illinois      wabash
## 1565 -88.0 38.5   653 23539 illinois      wabash
## 1566 -87.9 38.5   653 23540 illinois      wabash
## 1567 -87.9 38.5   653 23541 illinois      wabash
## 1568 -88.0 38.6   653 23542 illinois      wabash
## 1569 -90.4 41.1   654 23544 illinois      warren
## 1570 -90.4 40.7   654 23545 illinois      warren
## 1571 -90.4 40.6   654 23546 illinois      warren
## 1572 -90.8 40.6   654 23547 illinois      warren
## 1573 -90.8 41.1   654 23548 illinois      warren
## 1574 -90.4 41.1   654 23549 illinois      warren
## 1575 -89.1 38.5   655 23551 illinois  washington
## 1576 -89.1 38.2   655 23552 illinois  washington
## 1577 -89.4 38.2   655 23553 illinois  washington
## 1578 -89.5 38.2   655 23554 illinois  washington
## 1579 -89.6 38.2   655 23555 illinois  washington
## 1580 -89.7 38.2   655 23556 illinois  washington
## 1581 -89.7 38.4   655 23557 illinois  washington
## 1582 -89.7 38.4   655 23558 illinois  washington
## 1583 -89.6 38.4   655 23559 illinois  washington
## 1584 -89.6 38.5   655 23560 illinois  washington
## 1585 -89.6 38.5   655 23561 illinois  washington
## 1586 -89.6 38.5   655 23562 illinois  washington
## 1587 -89.6 38.5   655 23563 illinois  washington
## 1588 -89.5 38.5   655 23564 illinois  washington
## 1589 -89.5 38.5   655 23565 illinois  washington
## 1590 -89.5 38.5   655 23566 illinois  washington
## 1591 -89.4 38.5   655 23567 illinois  washington
## 1592 -89.4 38.5   655 23568 illinois  washington
## 1593 -89.4 38.5   655 23569 illinois  washington
## 1594 -89.3 38.5   655 23570 illinois  washington
## 1595 -89.3 38.5   655 23571 illinois  washington
## 1596 -89.2 38.5   655 23572 illinois  washington
## 1597 -89.1 38.5   655 23573 illinois  washington
## 1598 -89.1 38.5   655 23574 illinois  washington
## 1599 -88.2 38.6   656 23576 illinois       wayne
## 1600 -88.2 38.3   656 23577 illinois       wayne
## 1601 -88.4 38.3   656 23578 illinois       wayne
## 1602 -88.7 38.3   656 23579 illinois       wayne
## 1603 -88.7 38.5   656 23580 illinois       wayne
## 1604 -88.7 38.6   656 23581 illinois       wayne
## 1605 -88.3 38.6   656 23582 illinois       wayne
## 1606 -88.2 38.6   656 23583 illinois       wayne
## 1607 -88.2 38.6   656 23584 illinois       wayne
## 1608 -88.1 37.9   657 23586 illinois       white
## 1609 -88.1 37.9   657 23587 illinois       white
## 1610 -88.4 37.9   657 23588 illinois       white
## 1611 -88.4 38.3   657 23589 illinois       white
## 1612 -88.2 38.3   657 23590 illinois       white
## 1613 -88.0 38.3   657 23591 illinois       white
## 1614 -88.0 38.2   657 23592 illinois       white
## 1615 -88.0 38.2   657 23593 illinois       white
## 1616 -88.0 38.2   657 23594 illinois       white
## 1617 -88.0 38.2   657 23595 illinois       white
## 1618 -87.9 38.2   657 23596 illinois       white
## 1619 -87.9 38.2   657 23597 illinois       white
## 1620 -87.9 38.1   657 23598 illinois       white
## 1621 -88.0 38.1   657 23599 illinois       white
## 1622 -88.0 38.0   657 23600 illinois       white
## 1623 -88.0 38.0   657 23601 illinois       white
## 1624 -88.0 37.9   657 23602 illinois       white
## 1625 -88.0 37.9   657 23603 illinois       white
## 1626 -88.1 37.9   657 23604 illinois       white
## 1627 -88.1 37.9   657 23605 illinois       white
## 1628 -89.6 41.9   658 23607 illinois   whiteside
## 1629 -89.6 41.6   658 23608 illinois   whiteside
## 1630 -89.9 41.6   658 23609 illinois   whiteside
## 1631 -90.2 41.6   658 23610 illinois   whiteside
## 1632 -90.2 41.6   658 23611 illinois   whiteside
## 1633 -90.2 41.7   658 23612 illinois   whiteside
## 1634 -90.2 41.7   658 23613 illinois   whiteside
## 1635 -90.2 41.7   658 23614 illinois   whiteside
## 1636 -90.2 41.7   658 23615 illinois   whiteside
## 1637 -90.2 41.7   658 23616 illinois   whiteside
## 1638 -90.2 41.8   658 23617 illinois   whiteside
## 1639 -90.2 41.8   658 23618 illinois   whiteside
## 1640 -90.2 41.8   658 23619 illinois   whiteside
## 1641 -90.2 41.9   658 23620 illinois   whiteside
## 1642 -90.2 41.9   658 23621 illinois   whiteside
## 1643 -89.7 41.9   658 23622 illinois   whiteside
## 1644 -89.6 41.9   658 23623 illinois   whiteside
## 1645 -89.6 41.9   658 23624 illinois   whiteside
## 1646 -88.3 41.5   659 23626 illinois        will
## 1647 -88.3 41.7   659 23627 illinois        will
## 1648 -88.0 41.7   659 23628 illinois        will
## 1649 -88.0 41.7   659 23629 illinois        will
## 1650 -88.0 41.6   659 23630 illinois        will
## 1651 -87.9 41.6   659 23631 illinois        will
## 1652 -87.9 41.6   659 23632 illinois        will
## 1653 -87.8 41.6   659 23633 illinois        will
## 1654 -87.8 41.5   659 23634 illinois        will
## 1655 -87.5 41.5   659 23635 illinois        will
## 1656 -87.5 41.3   659 23636 illinois        will
## 1657 -88.0 41.3   659 23637 illinois        will
## 1658 -88.0 41.2   659 23638 illinois        will
## 1659 -88.2 41.2   659 23639 illinois        will
## 1660 -88.3 41.5   659 23640 illinois        will
## 1661 -89.0 37.6   660 23642 illinois  williamson
## 1662 -89.2 37.6   660 23643 illinois  williamson
## 1663 -89.2 37.9   660 23644 illinois  williamson
## 1664 -88.7 37.9   660 23645 illinois  williamson
## 1665 -88.7 37.6   660 23646 illinois  williamson
## 1666 -89.0 37.6   660 23647 illinois  williamson
## 1667 -89.4 42.2   661 23649 illinois   winnebago
## 1668 -89.4 42.5   661 23650 illinois   winnebago
## 1669 -89.4 42.5   661 23651 illinois   winnebago
## 1670 -88.9 42.5   661 23652 illinois   winnebago
## 1671 -88.9 42.2   661 23653 illinois   winnebago
## 1672 -89.2 42.2   661 23654 illinois   winnebago
## 1673 -89.2 42.2   661 23655 illinois   winnebago
## 1674 -89.4 42.2   661 23656 illinois   winnebago
## 1675 -89.0 40.9   662 23658 illinois    woodford
## 1676 -88.9 40.9   662 23659 illinois    woodford
## 1677 -88.9 40.7   662 23660 illinois    woodford
## 1678 -89.0 40.7   662 23661 illinois    woodford
## 1679 -89.0 40.7   662 23662 illinois    woodford
## 1680 -89.0 40.7   662 23663 illinois    woodford
## 1681 -89.0 40.6   662 23664 illinois    woodford
## 1682 -89.1 40.6   662 23665 illinois    woodford
## 1683 -89.1 40.6   662 23666 illinois    woodford
## 1684 -89.1 40.6   662 23667 illinois    woodford
## 1685 -89.1 40.6   662 23668 illinois    woodford
## 1686 -89.3 40.6   662 23669 illinois    woodford
## 1687 -89.3 40.6   662 23670 illinois    woodford
## 1688 -89.3 40.6   662 23671 illinois    woodford
## 1689 -89.3 40.7   662 23672 illinois    woodford
## 1690 -89.6 40.8   662 23673 illinois    woodford
## 1691 -89.6 40.8   662 23674 illinois    woodford
## 1692 -89.6 40.8   662 23675 illinois    woodford
## 1693 -89.5 40.8   662 23676 illinois    woodford
## 1694 -89.5 40.9   662 23677 illinois    woodford
## 1695 -89.5 40.9   662 23678 illinois    woodford
## 1696 -89.0 40.9   662 23679 illinois    woodford

Plot the state first. This time, lets’ remove all the axes gridlines and background junk using theme_void():

il_base <- ggplot(data = il_df, mapping = aes(x = long, y = lat, group = group)) + 
  coord_fixed(1.3) + 
  geom_polygon(color = "black", fill = "gray")

il_base +
  theme_nothing()

Now let’s plot the county boundaries in white:

il_base +
  theme_void() + 
  geom_polygon(data = il_county, fill = NA, color = "white") +
  geom_polygon(color = "black", fill = NA)  # get the state border back on top

But what about Alaska and Hawaii?

If you were observant, you noticed map_data("states") only includes the 48 contiguous states in the United States. This is because Alaska and Hawaii exist far off from the rest of the states. What happens if you try to draw a map with them in it?

Yup, that doesn’t look right. Most maps of the United States place Alaska and Hawaii as insets to the south of California. Until recently, in R this was an extremely tedious task that required manually changing the latitude and longitude coordinates for these states to place them in the correct location. Fortunately a new package is available that has already done the work for you. fiftystater includes the fifty_states data frame which contains adjusted coordinates for Alaska and Hawaii to plot them with the mainland:

library(fiftystater)

data("fifty_states")
fifty_states %>%
  as_tibble
## # A tibble: 13,694 × 7
##     long   lat order  hole  piece      id     group
##    <dbl> <dbl> <int> <lgl> <fctr>   <chr>    <fctr>
##  1 -85.1  32.0     1 FALSE      1 alabama Alabama.1
##  2 -85.1  31.9     2 FALSE      1 alabama Alabama.1
##  3 -85.1  31.9     3 FALSE      1 alabama Alabama.1
##  4 -85.1  31.8     4 FALSE      1 alabama Alabama.1
##  5 -85.1  31.8     5 FALSE      1 alabama Alabama.1
##  6 -85.1  31.7     6 FALSE      1 alabama Alabama.1
##  7 -85.1  31.7     7 FALSE      1 alabama Alabama.1
##  8 -85.1  31.7     8 FALSE      1 alabama Alabama.1
##  9 -85.1  31.6     9 FALSE      1 alabama Alabama.1
## 10 -85.0  31.6    10 FALSE      1 alabama Alabama.1
## # ... with 13,684 more rows
ggplot(data = fifty_states, mapping = aes(x = long, y = lat, group = group)) + 
  coord_fixed(1.3) + 
  geom_polygon(color = "black", fill = "gray")

Using shapefiles

maps contains a very limited number of geodatabases. If you want to import a different country’s borders or some other geographic information, you will likely find the data in a shapefile. This is a special file format that encodes points, lines, and polygons in geographic space. Files appear with a .shp extension, sometimes with accompanying files ending in .dbf and .prj.

  • .shp stores the geographic coordinates of the geographic features (e.g. country, state, county)
  • .dbf stores data associated with the geographic features (e.g. unemployment rate, crime rates, percentage of votes cast for Donald Trump)
  • .prj stores information about the projection of the coordinates in the shapefile (we’ll handle this shortly)

Let’s start with a shapefile for state boundaries in the United States.4 We’ll use readShapePoly() from the maptools package to read in the data file:

library(rgdal)

usa <- readOGR("data/census_bureau/cb_2013_us_state_20m/cb_2013_us_state_20m.shp")
## OGR data source with driver: ESRI Shapefile 
## Source: "data/census_bureau/cb_2013_us_state_20m/cb_2013_us_state_20m.shp", layer: "cb_2013_us_state_20m"
## with 52 features
## It has 9 fields
## Integer64 fields read as strings:  ALAND AWATER
str(usa, max.level = 2)
## Formal class 'SpatialPolygonsDataFrame' [package "sp"] with 5 slots
##   ..@ data       :'data.frame':  52 obs. of  9 variables:
##   ..@ polygons   :List of 52
##   ..@ plotOrder  : int [1:52] 32 48 29 3 18 46 33 22 34 51 ...
##   ..@ bbox       : num [1:2, 1:2] -179.1 17.9 179.8 71.4
##   .. ..- attr(*, "dimnames")=List of 2
##   ..@ proj4string:Formal class 'CRS' [package "sp"] with 1 slot

This is decidedly not a tidy data frame. Once you import the shapefile, it’s best to convert it to a data frame for ggplot(). We can do this using fortify():

usa %>%
  fortify() %>%
  head
##    long  lat order  hole piece id group
## 1 -88.5 31.9     1 FALSE     1  0   0.1
## 2 -88.5 31.9     2 FALSE     1  0   0.1
## 3 -88.5 31.9     3 FALSE     1  0   0.1
## 4 -88.5 31.9     4 FALSE     1  0   0.1
## 5 -88.5 32.0     5 FALSE     1  0   0.1
## 6 -88.5 32.0     6 FALSE     1  0   0.1

Under this approach, the id variable is just a number assigned to each region (in this case, each state/territory). However the shapefile contains linked data with attributes for each region. We can access this using the @data accessor:

usa@data %>%
  as_tibble
## # A tibble: 52 × 9
##    STATEFP  STATENS    AFFGEOID  GEOID STUSPS        NAME   LSAD
##  *  <fctr>   <fctr>      <fctr> <fctr> <fctr>      <fctr> <fctr>
##  1      01 01779775 0400000US01     01     AL     Alabama     00
##  2      05 00068085 0400000US05     05     AR    Arkansas     00
##  3      06 01779778 0400000US06     06     CA  California     00
##  4      09 01779780 0400000US09     09     CT Connecticut     00
##  5      12 00294478 0400000US12     12     FL     Florida     00
##  6      13 01705317 0400000US13     13     GA     Georgia     00
##  7      16 01779783 0400000US16     16     ID       Idaho     00
##  8      17 01779784 0400000US17     17     IL    Illinois     00
##  9      18 00448508 0400000US18     18     IN     Indiana     00
## 10      20 00481813 0400000US20     20     KS      Kansas     00
## # ... with 42 more rows, and 2 more variables: ALAND <fctr>, AWATER <fctr>

We can keep these variables in the new data frame through parameters to fortify(region = ""):

# state name
usa %>%
  fortify(region = "NAME") %>%
  head
##    long  lat order  hole piece      id     group
## 1 -88.5 31.9     1 FALSE     1 Alabama Alabama.1
## 2 -88.5 31.9     2 FALSE     1 Alabama Alabama.1
## 3 -88.5 31.9     3 FALSE     1 Alabama Alabama.1
## 4 -88.5 31.9     4 FALSE     1 Alabama Alabama.1
## 5 -88.5 32.0     5 FALSE     1 Alabama Alabama.1
## 6 -88.5 32.0     6 FALSE     1 Alabama Alabama.1
# FIPS code
usa %>%
  fortify(region = "STATEFP") %>%
  head
##    long  lat order  hole piece id group
## 1 -88.5 31.9     1 FALSE     1 01  01.1
## 2 -88.5 31.9     2 FALSE     1 01  01.1
## 3 -88.5 31.9     3 FALSE     1 01  01.1
## 4 -88.5 31.9     4 FALSE     1 01  01.1
## 5 -88.5 32.0     5 FALSE     1 01  01.1
## 6 -88.5 32.0     6 FALSE     1 01  01.1
# keep it all
(usa2 <- usa %>%
  fortify(region = "NAME") %>%
  as_tibble %>%
  left_join(usa@data, by = c("id" = "NAME")))
## # A tibble: 46,174 × 15
##     long   lat order  hole  piece      id     group STATEFP  STATENS
##    <dbl> <dbl> <int> <lgl> <fctr>   <chr>    <fctr>  <fctr>   <fctr>
##  1 -88.5  31.9     1 FALSE      1 Alabama Alabama.1      01 01779775
##  2 -88.5  31.9     2 FALSE      1 Alabama Alabama.1      01 01779775
##  3 -88.5  31.9     3 FALSE      1 Alabama Alabama.1      01 01779775
##  4 -88.5  31.9     4 FALSE      1 Alabama Alabama.1      01 01779775
##  5 -88.5  32.0     5 FALSE      1 Alabama Alabama.1      01 01779775
##  6 -88.5  32.0     6 FALSE      1 Alabama Alabama.1      01 01779775
##  7 -88.5  32.1     7 FALSE      1 Alabama Alabama.1      01 01779775
##  8 -88.4  32.2     8 FALSE      1 Alabama Alabama.1      01 01779775
##  9 -88.4  32.2     9 FALSE      1 Alabama Alabama.1      01 01779775
## 10 -88.4  32.2    10 FALSE      1 Alabama Alabama.1      01 01779775
## # ... with 46,164 more rows, and 6 more variables: AFFGEOID <fctr>,
## #   GEOID <fctr>, STUSPS <fctr>, LSAD <fctr>, ALAND <fctr>, AWATER <fctr>

Now we can plot it like normal using ggplot():

ggplot(data = usa2, mapping = aes(x = long, y = lat, group = group)) + 
  coord_fixed(1.3) + 
  geom_polygon(color = "black", fill = "gray")

Because the data file comes from the Census Bureau, we also get boundaries for Alaska, Hawaii, and Puerto Rico. To remove them from the data, just use filter():

usa2 <- usa2 %>%
  filter(id != "Alaska", id != "Hawaii", id != "Puerto Rico")

ggplot(data = usa2, mapping = aes(x = long, y = lat, group = group)) + 
  coord_fixed(1.3) + 
  geom_polygon(color = "black", fill = "gray")

ggmap

Rather than relying on geodatabases or shapefiles which store boundaries as numeric data, we can use ggmap to retrieve raster map tiles from online mapping services.

library(ggmap)
get_stamenmap(c(left = -87.6189, bottom = 41.7723, right = -87.5721, top = 41.8107), zoom = 14) %>%
  ggmap()    # NOTE: this will generate an error with ggplot2 v.2.2.1+

get_openstreetmap(bbox = c(left = -87.6189, bottom = 41.7723, right = -87.5721, top = 41.8107)) %>%
  ggmap()
## Error: map grabbing failed - see details in ?get_openstreetmap.
get_googlemap("university of chicago", zoom = 12) %>%
  ggmap()

Changing map projections

As you saw in The Truthful Art, representing portions of the globe on a flat surface can be challenging. Depending on how you project the map, you can distort or emphasize certain features of the map. Fortunately, ggplot() includes the coord_map() function which allows us to easily implement different projection methods.5

ggplot(data = usa2, mapping = aes(x = long, y = lat, group = group)) + 
  geom_polygon(color = "black", fill = "gray") +
  coord_map() +
  ggtitle("Mercator projection (default)")

ggplot(data = usa2, mapping = aes(x = long, y = lat, group = group)) + 
  geom_polygon(color = "black", fill = "gray") +
  coord_map(projection = "albers", lat0 = 25, lat1 = 50) +
  ggtitle("Albers equal-area projection")

ggplot(data = usa2, mapping = aes(x = long, y = lat, group = group)) + 
  geom_polygon(color = "black", fill = "gray") +
  coord_map(projection = "lambert", lat0 = 25, lat1 = 50) +
  ggtitle("Lambert equal-area projection")

ggplot(data = usa2, mapping = aes(x = long, y = lat, group = group)) + 
  geom_polygon(color = "black", fill = "gray") +
  coord_map(projection = "conic", lat0 = 40) +
  ggtitle("Conic projection")

ggplot(data = usa2, mapping = aes(x = long, y = lat, group = group)) + 
  geom_polygon(color = "black", fill = "gray") +
  coord_map(projection = "mollweide") +
  ggtitle("Mollweide projection")

Adding data to the map

Region boundaries serve as the background in geospatial data visualization - so now we need to add data. Some types of channels (points, symbols, and lines) are overlaid on top of the boundaries, whereas other channels (fill) are incorporated into the region layer itself. Let’s look at the first set of channels.

Points

Let’s use our usa2 map data to add some points. The airports data frame in the nycflights13 package includes geographic info on airports in the United States.

library(nycflights13)
airports
## # A tibble: 1,458 × 8
##      faa                           name   lat    lon   alt    tz   dst
##    <chr>                          <chr> <dbl>  <dbl> <int> <dbl> <chr>
##  1   04G              Lansdowne Airport  41.1  -80.6  1044    -5     A
##  2   06A  Moton Field Municipal Airport  32.5  -85.7   264    -6     A
##  3   06C            Schaumburg Regional  42.0  -88.1   801    -6     A
##  4   06N                Randall Airport  41.4  -74.4   523    -5     A
##  5   09J          Jekyll Island Airport  31.1  -81.4    11    -5     A
##  6   0A9 Elizabethton Municipal Airport  36.4  -82.2  1593    -5     A
##  7   0G6        Williams County Airport  41.5  -84.5   730    -5     A
##  8   0G7  Finger Lakes Regional Airport  42.9  -76.8   492    -5     A
##  9   0P2   Shoestring Aviation Airfield  39.8  -76.6  1000    -5     U
## 10   0S9          Jefferson County Intl  48.1 -122.8   108    -8     A
## # ... with 1,448 more rows, and 1 more variables: tzone <chr>

Each airport has it’s geographic location encoded through lat and lon. To draw these points on the map, basically we draw a scatterplot with x = lon and y = lat. In fact we could simply do that:

ggplot(airports, aes(lon, lat)) +
  geom_point()

Let’s overlay it with the mapped state borders:

ggplot() + 
  coord_map() + 
  geom_polygon(data = usa2, mapping = aes(x = long, y = lat, group = group),
               color = "black", fill = "gray") +
  geom_point(data = airports, aes(x = lon, y = lat), shape = 1)

Slight problem. We have airports listed outside of the continental United States. Rather than trying to filter out airports, we can just crop the limits of the graph to only show the mainland:

ggplot() + 
  coord_map(xlim = c(-130, -60),
            ylim = c(20, 50)) + 
  geom_polygon(data = usa2, mapping = aes(x = long, y = lat, group = group),
               color = "black", fill = "gray") +
  geom_point(data = airports, aes(x = lon, y = lat), shape = 1)

If we want to change the projection method, the points will automatically adjust too:

ggplot() + 
  coord_map(projection = "albers", lat0 = 25, lat1 = 50,
            xlim = c(-130, -60),
            ylim = c(20, 50)) + 
  geom_polygon(data = usa2, mapping = aes(x = long, y = lat, group = group),
               color = "black", fill = "gray") +
  geom_point(data = airports, aes(x = lon, y = lat), shape = 1)

Symbols

We can change the size or type of symbols on the map. For instance, we can draw a bubble plot (also known as a proportional symbol map) and encode the altitude of the airport through the size channel:

ggplot() + 
  coord_map(xlim = c(-130, -60),
            ylim = c(20, 50)) + 
  geom_polygon(data = usa2, mapping = aes(x = long, y = lat, group = group),
               color = "black", fill = "white") +
  geom_point(data = airports, aes(x = lon, y = lat, size = alt),
             fill = "grey", color = "black", alpha = .2) +
  theme_void() +
  theme(legend.position = "none")

Circle area is proportional to the airport’s altitude (in feet).

Lines

Drawing choropleth maps

Choropleth maps encode information by assigning shades of colors to defined areas on a map (e.g. countries, states, counties, zip codes). There are lots of ways to tweak and customize these graphs, which is generally a good idea because remember that color is one of the harder-to-decode channels.

Loading the data

We’ll continue to use the usa2 shapefile. Let’s also load and tidy the county shapefile:

usa <- readOGR("data/census_bureau/cb_2013_us_state_20m/cb_2013_us_state_20m.shp")
## OGR data source with driver: ESRI Shapefile 
## Source: "data/census_bureau/cb_2013_us_state_20m/cb_2013_us_state_20m.shp", layer: "cb_2013_us_state_20m"
## with 52 features
## It has 9 fields
## Integer64 fields read as strings:  ALAND AWATER
usa2 <- usa %>%
  fortify(region = "GEOID") %>%
  as_tibble %>%
  left_join(usa@data, by = c("id" = "GEOID")) %>%
  # filter out Alaska, Hawaii, Puerto Rico via FIPS codes
  filter(!(STATEFP %in% c("02", "15", "72")))

counties <- readOGR("data/census_bureau/cb_2013_us_county_20m/cb_2013_us_county_20m.shp")
## OGR data source with driver: ESRI Shapefile 
## Source: "data/census_bureau/cb_2013_us_county_20m/cb_2013_us_county_20m.shp", layer: "cb_2013_us_county_20m"
## with 3221 features
## It has 9 fields
## Integer64 fields read as strings:  ALAND AWATER
counties2 <- counties %>%
  fortify(region = "GEOID") %>%
  as_tibble %>%
  left_join(counties@data, by = c("id" = "GEOID")) %>%
  # filter out Alaska, Hawaii, Puerto Rico via FIPS codes
  filter(!(STATEFP %in% c("02", "15", "72")))

ggplot(counties2, mapping = aes(x = long, y = lat, group = group)) + 
  geom_polygon(color = "black", fill = "gray") +
  coord_map()

We’ll draw choropleths for the number of foreign-born individuals in each region (state or county). We can get those files from the census_bureau folder. Let’s also normalize our measure by the total population to get the rate of foreign-born individuals in the population:

(fb_state <- read_csv("data/census_bureau/ACS_13_5YR_B05012_state/ACS_13_5YR_B05012.csv") %>%
  mutate(rate = HD01_VD03 / HD01_VD01))
## # A tibble: 51 × 10
##         GEO.id GEO.id2  `GEO.display-label` HD01_VD01 HD02_VD01 HD01_VD02
##          <chr>   <chr>                <chr>     <int>     <chr>     <int>
##  1 0400000US01      01              Alabama   4799277      <NA>   4631045
##  2 0400000US02      02               Alaska    720316      <NA>    669941
##  3 0400000US04      04              Arizona   6479703      <NA>   5609835
##  4 0400000US05      05             Arkansas   2933369      <NA>   2799972
##  5 0400000US06      06           California  37659181      <NA>  27483342
##  6 0400000US08      08             Colorado   5119329      <NA>   4623809
##  7 0400000US09      09          Connecticut   3583561      <NA>   3096374
##  8 0400000US10      10             Delaware    908446      <NA>    831683
##  9 0400000US11      11 District of Columbia    619371      <NA>    534142
## 10 0400000US12      12              Florida  19091156      <NA>  15392410
## # ... with 41 more rows, and 4 more variables: HD02_VD02 <int>,
## #   HD01_VD03 <int>, HD02_VD03 <int>, rate <dbl>
(fb_county <- read_csv("data/census_bureau/ACS_13_5YR_B05012_county/ACS_13_5YR_B05012.csv") %>%
  mutate(rate = HD01_VD03 / HD01_VD01))
## # A tibble: 3,143 × 10
##            GEO.id GEO.id2      `GEO.display-label` HD01_VD01 HD02_VD01
##             <chr>   <chr>                    <chr>     <int>     <int>
##  1 0500000US01001   01001  Autauga County, Alabama     54907        NA
##  2 0500000US01003   01003  Baldwin County, Alabama    187114        NA
##  3 0500000US01005   01005  Barbour County, Alabama     27321        NA
##  4 0500000US01007   01007     Bibb County, Alabama     22754        NA
##  5 0500000US01009   01009   Blount County, Alabama     57623        NA
##  6 0500000US01011   01011  Bullock County, Alabama     10746        NA
##  7 0500000US01013   01013   Butler County, Alabama     20624        NA
##  8 0500000US01015   01015  Calhoun County, Alabama    117714        NA
##  9 0500000US01017   01017 Chambers County, Alabama     34145        NA
## 10 0500000US01019   01019 Cherokee County, Alabama     26034        NA
## # ... with 3,133 more rows, and 5 more variables: HD01_VD02 <int>,
## #   HD02_VD02 <int>, HD01_VD03 <int>, HD02_VD03 <int>, rate <dbl>

Joining the data to regions

Now that we have our data, we want to draw it on the map. To do that, we have to join together our data sources - the shapefiles and the CSVs. Normally joining data files requires a _join() operation of some sort. However when using ggplot2, we don’t have to do this. Remember that we can pass different data frames into different layers of a ggplot() object. Rather than using geom_polygon() to draw our maps, now we switch to geom_map():

ggplot(fb_state, aes(map_id = GEO.id2)) +
  geom_map(aes(fill = rate), map = usa2) +
  expand_limits(x = usa2$long, y = usa2$lat)

Let’s break down what just happened:

  • fb_state is the data frame with the variables we want to visualize
  • map_id = GEO.id2 identifies the column in fb_state that uniquely matches each observation to a region in usa2
  • geom_map(aes(fill = rate), map = usa2)
    • map = usa2 is the data frame containing the boundary coordinates
    • fill = rate identifies the column in fb_state we will use to determine the color of each region
  • expand_limits(x = usa2$long, y = usa2$lat) ensures the graph is drawn to the proper window. Because the default data frame for this ggplot() object is fb_state, it won’t contain the necessary information to size the window

We can then tweak this up by adding a title, removing the background (but retaining the legend), and projecting the map using a different method:

ggplot(fb_state, aes(map_id = GEO.id2)) +
  geom_map(aes(fill = rate), map = usa2) +
  expand_limits(x = usa2$long, y = usa2$lat) +
  scale_fill_continuous(labels = scales::percent) +
  labs(title = "Rate of foreign-born individuals in the population",
       fill = NULL) +
  ggthemes::theme_map() +
  coord_map(projection = "albers", lat0 = 25, lat1 = 50)

We could do the same thing for the county-level data:

ggplot(fb_county, aes(map_id = GEO.id2)) +
  geom_map(aes(fill = rate), map = counties2) +
  expand_limits(x = counties2$long, y = counties2$lat) +
  scale_fill_continuous(labels = scales::percent) +
  labs(title = "Rate of foreign-born individuals in the population",
       fill = NULL) +
  ggthemes::theme_map() +
  coord_map(projection = "albers", lat0 = 25, lat1 = 50)

Binning data

  • cut_interval() - makes n groups with equal range
fb_county %>%
  mutate(rate_cut = cut_interval(rate, 6)) %>%
  ggplot(aes(map_id = GEO.id2)) +
  geom_map(aes(fill = rate_cut), map = counties2) +
  expand_limits(x = counties2$long, y = counties2$lat) +
  labs(title = "Rate of foreign-born individuals in the population",
       fill = NULL) +
  ggthemes::theme_map() +
  coord_map(projection = "albers", lat0 = 25, lat1 = 50)

  • cut_number() - makes n groups with (approximately) equal numbers of observations
fb_county %>%
  mutate(rate_cut = cut_number(rate, 6)) %>%
  ggplot(aes(map_id = GEO.id2)) +
  geom_map(aes(fill = rate_cut), map = counties2) +
  expand_limits(x = counties2$long, y = counties2$lat) +
  labs(title = "Rate of foreign-born individuals in the population",
       fill = NULL) +
  ggthemes::theme_map() +
  coord_map(projection = "albers", lat0 = 25, lat1 = 50)

Defining colors

RColorBrewer

Sequential

fb_county %>%
  mutate(rate_cut = cut_number(rate, 6)) %>%
  ggplot(aes(map_id = GEO.id2)) +
  geom_map(aes(fill = rate_cut), map = counties2) +
  expand_limits(x = counties2$long, y = counties2$lat) +
  labs(title = "Rate of foreign-born individuals in the population",
       fill = NULL) +
  ggthemes::theme_map() +
  coord_map(projection = "albers", lat0 = 25, lat1 = 50) +
  scale_fill_brewer(palette = "BuGn")

fb_county %>%
  mutate(rate_cut = cut_number(rate, 6)) %>%
  ggplot(aes(map_id = GEO.id2)) +
  geom_map(aes(fill = rate_cut), map = counties2) +
  expand_limits(x = counties2$long, y = counties2$lat) +
  labs(title = "Rate of foreign-born individuals in the population",
       fill = NULL) +
  ggthemes::theme_map() +
  coord_map(projection = "albers", lat0 = 25, lat1 = 50) +
  scale_fill_brewer(palette = "YlGn")

fb_county %>%
  mutate(rate_cut = cut_number(rate, 6)) %>%
  ggplot(aes(map_id = GEO.id2)) +
  geom_map(aes(fill = rate_cut), map = counties2) +
  expand_limits(x = counties2$long, y = counties2$lat) +
  labs(title = "Rate of foreign-born individuals in the population",
       fill = NULL) +
  ggthemes::theme_map() +
  coord_map(projection = "albers", lat0 = 25, lat1 = 50) +
  scale_fill_brewer(palette = "Blues")

Qualitative

state_data <- data_frame(name = state.name,
                         region = state.region,
                         subregion = state.division) %>%
  bind_cols(as_tibble(state.x77)) %>%
  # get id variable into data frame
  left_join(usa2 %>%
              select(id, NAME) %>%
              distinct,
            by = c("name" = "NAME")) %>%
  # remove Alaska and Hawaii
  na.omit

# set region base plot
region_p <- ggplot(state_data, aes(map_id = id)) +
  geom_map(aes(fill = region), map = usa2) +
  expand_limits(x = usa2$long, y = usa2$lat) +
  labs(fill = NULL) +
  ggthemes::theme_map() +
  coord_map(projection = "albers", lat0 = 25, lat1 = 50)
region_p

# try different color brewers
region_p +
  scale_fill_brewer(palette = "Paired")

region_p +
  scale_fill_brewer(palette = "Dark2")

region_p +
  scale_fill_brewer(palette = "Pastel2")

# set subregion base plot
subregion_p <- ggplot(state_data, aes(map_id = id)) +
  geom_map(aes(fill = subregion), map = usa2) +
  expand_limits(x = usa2$long, y = usa2$lat) +
  labs(fill = NULL) +
  ggthemes::theme_map() +
  coord_map(projection = "albers", lat0 = 25, lat1 = 50)
subregion_p

subregion_p +
  scale_fill_brewer(palette = "Paired")

subregion_p +
  scale_fill_brewer(palette = "Set1")

subregion_p +
  scale_fill_brewer(palette = "Pastel1")

Fixed size choropleths

Faceting maps

Making maps interactive

ggplotly()

leaflet

Session Info

devtools::session_info()
##  setting  value                       
##  version  R version 3.3.3 (2017-03-06)
##  system   x86_64, darwin13.4.0        
##  ui       X11                         
##  language (EN)                        
##  collate  en_US.UTF-8                 
##  tz       America/Chicago             
##  date     2017-05-05                  
## 
##  package     * version    date       source                           
##  assertthat    0.2.0      2017-04-11 cran (@0.2.0)                    
##  backports     1.0.5      2017-01-18 CRAN (R 3.3.2)                   
##  bitops        1.0-6      2013-08-17 CRAN (R 3.3.0)                   
##  broom       * 0.4.2      2017-02-13 CRAN (R 3.3.2)                   
##  colorspace    1.3-2      2016-12-14 CRAN (R 3.3.2)                   
##  DBI           0.6        2017-03-09 CRAN (R 3.3.3)                   
##  devtools      1.12.0     2016-06-24 CRAN (R 3.3.0)                   
##  digest        0.6.12     2017-01-27 CRAN (R 3.3.2)                   
##  dplyr       * 0.5.0      2016-06-24 CRAN (R 3.3.0)                   
##  evaluate      0.10       2016-10-11 CRAN (R 3.3.0)                   
##  forcats     * 0.2.0      2017-01-23 CRAN (R 3.3.2)                   
##  foreign       0.8-67     2016-09-13 CRAN (R 3.3.3)                   
##  geosphere     1.5-5      2016-06-15 CRAN (R 3.3.0)                   
##  ggmap       * 2.7        2016-12-07 Github (dkahle/ggmap@c6b7579)    
##  ggplot2     * 2.2.1      2016-12-30 CRAN (R 3.3.2)                   
##  gtable        0.2.0      2016-02-26 CRAN (R 3.3.0)                   
##  haven         1.0.0      2016-09-23 cran (@1.0.0)                    
##  hms           0.3        2016-11-22 CRAN (R 3.3.2)                   
##  htmltools     0.3.6      2017-04-28 cran (@0.3.6)                    
##  htmlwidgets   0.8        2016-11-09 CRAN (R 3.3.1)                   
##  httr          1.2.1      2016-07-03 CRAN (R 3.3.0)                   
##  jpeg          0.1-8      2014-01-23 cran (@0.1-8)                    
##  jsonlite      1.4        2017-04-08 cran (@1.4)                      
##  knitr       * 1.15.1     2016-11-22 cran (@1.15.1)                   
##  lattice       0.20-34    2016-09-06 CRAN (R 3.3.3)                   
##  lazyeval      0.2.0      2016-06-12 CRAN (R 3.3.0)                   
##  lubridate     1.6.0      2016-09-13 CRAN (R 3.3.0)                   
##  magrittr      1.5        2014-11-22 CRAN (R 3.3.0)                   
##  mapproj       1.2-4      2015-08-03 CRAN (R 3.3.0)                   
##  maps          3.1.1      2016-07-27 CRAN (R 3.3.0)                   
##  memoise       1.0.0      2016-01-29 CRAN (R 3.3.0)                   
##  mnormt        1.5-5      2016-10-15 CRAN (R 3.3.0)                   
##  modelr      * 0.1.0      2016-08-31 CRAN (R 3.3.0)                   
##  munsell       0.4.3      2016-02-13 CRAN (R 3.3.0)                   
##  nlme          3.1-131    2017-02-06 CRAN (R 3.3.3)                   
##  plotly      * 4.6.0      2017-04-25 CRAN (R 3.3.3)                   
##  plyr          1.8.4      2016-06-08 CRAN (R 3.3.0)                   
##  png           0.1-7      2013-12-03 cran (@0.1-7)                    
##  proto         1.0.0      2016-10-29 CRAN (R 3.3.0)                   
##  psych         1.7.3.21   2017-03-22 CRAN (R 3.3.2)                   
##  purrr       * 0.2.2      2016-06-18 CRAN (R 3.3.0)                   
##  R6            2.2.0      2016-10-05 CRAN (R 3.3.0)                   
##  Rcpp          0.12.10    2017-03-19 cran (@0.12.10)                  
##  readr       * 1.1.0      2017-03-22 cran (@1.1.0)                    
##  readxl        0.1.1      2016-03-28 CRAN (R 3.3.0)                   
##  reshape2      1.4.2      2016-10-22 CRAN (R 3.3.0)                   
##  RgoogleMaps   1.4.1      2016-09-18 cran (@1.4.1)                    
##  rjson         0.2.15     2014-11-03 cran (@0.2.15)                   
##  rlang         0.0.0.9018 2017-05-01 Github (hadley/rlang@460323e)    
##  rmarkdown     1.3        2016-12-21 CRAN (R 3.3.2)                   
##  rprojroot     1.2        2017-01-16 CRAN (R 3.3.2)                   
##  rvest         0.3.2      2016-06-17 CRAN (R 3.3.0)                   
##  scales        0.4.1      2016-11-09 CRAN (R 3.3.1)                   
##  sp            1.2-4      2016-12-22 CRAN (R 3.3.2)                   
##  stringi       1.1.2      2016-10-01 CRAN (R 3.3.0)                   
##  stringr     * 1.2.0      2017-02-18 CRAN (R 3.3.2)                   
##  tibble      * 1.3.0.9001 2017-05-01 Github (tidyverse/tibble@08af6b0)
##  tidyr       * 0.6.1      2017-01-10 CRAN (R 3.3.2)                   
##  tidyverse   * 1.1.1      2017-01-27 CRAN (R 3.3.2)                   
##  viridisLite   0.2.0      2017-03-24 cran (@0.2.0)                    
##  withr         1.0.2      2016-06-20 CRAN (R 3.3.0)                   
##  xml2          1.1.1      2017-01-24 CRAN (R 3.3.2)                   
##  yaml          2.1.14     2016-11-12 cran (@2.1.14)

  1. See chapter 10 in The Truthful Art for a more detailed introduction to these features.

  2. Source: Wikipedia.

  3. Or as we’ll see shortly, apply a map projection to the graph.

  4. Originally obtained from the Census Bureau.

  5. This function replaces coord_fixed().